Identifying the source camera of images and videos has gained significant importance in multimedia forensics. It allows tracing back data to their creator, thus enabling to solve copyright infringement cases and expose the authors of hideous crimes. In this paper, we focus on the problem of camera model identification for video sequences, that is, given a video under analysis, detecting the camera model used for its acquisition. To this purpose, we develop two different CNN-based camera model identification methods, working in a novel multi-modal scenario. Differently from mono-modal methods, which use only the visual or audio information from the investigated video to tackle the identification task, the proposed multi-modal methods jointly exploit audio and visual information. We test our proposed methodologies on the well-known Vision dataset, which collects almost 2000 video sequences belonging to different devices. Experiments are performed, considering native videos directly acquired by their acquisition devices and videos uploaded on social media platforms, such as YouTube and WhatsApp. The achieved results show that the proposed multi-modal approaches significantly outperform their mono-modal counterparts, representing a valuable strategy for the tackled problem and opening future research to even more challenging scenarios. Camera model identification has gained significant importance in multimedia forensic investigations as digital multimedia contents  are increasingly widespread and will continue to spread in the future with the advance of technological progress. This phenomenon is mainly attributable to the advent of the internet and social media, which have allowed a very rapid diffusion of digital contents and, consequently, made it extremely difficult to trace their origin.For instance, in forensic investigations, tracing the origin of digital contents can be essential to identify the perpetrators of such crimes as rape, drug trafficking or acts of terrorism. There is also the possibility that certain private content become viral through the internet, as has sadly happened in recent times with revenge porn. Being able to retrieve the source of multimedia content, therefore, assumes a fundamental role.This paper aims at determining the smartphone model used to acquire digital video sequences by jointly exploiting visual and audio information from the videos themselves. We mainly focus on video source identification because little work has been done specifically for digital video sequences in the forensic literature . On the Model-based approaches specifically rely on exploiting the traces released by the digital image acquisition process in order to identify the camera model. Several works in the literature exploit specific features associated with the Color Filter Array (CFA) configuration  ,4 and thIn the last few years, the availability of digital data and computational resources has lead to the growth of data-driven approaches, which have greatly outperformed many model-based solutions proposed in the past. Instead of focusing on a specific trace left by the image acquisition process, as is typically done in model-based methodologies, data-driven approaches are able to capture model traces, due to various components’ interplay . The mosTo our knowledge, the only work that investigates the problem of camera model identification on video sequences is proposed in . The autIn this paper, we rely on advanced deep-learning approaches to develop effective methods for camera model identification on video sequences. Specifically, our proposed method involves the use of CNNs capable of classifying videos by jointly extracting suitable features from their visual and audio content. We define the proposed strategy as multi-modal since we exploit both visual and audio information coming from the query video to solve the identification task. Given a video, as visual content, we use patches cropped from the frames; as audio content, we use patches cropped from the Log-Mel Spectrogram (LMS) of its audio track. In this vein, the approach suggested by  falls inWe propose two distinct multi-modal camera model identification approaches. In both proposed approaches, we make use of CNNs and feed them with pairs of visual and audio patches. In the first approach, we compare and fuse the scores individually obtained from two CNNs, trained following a mono-modal strategy, i.e., one CNN only deals with visual information and the other one only with audio. In the second approach, we train a single multi-input CNN, which deals with both visual and audio patches. Moreover, for each proposed approach, we investigate three different network configurations and data pre-processings, exploiting effective CNN architectures that are well known in the state of the art ,24.We evaluate results on the Vision dataset, which contains approximately 650 native video sequences with their related social media versions, collecting almost 2000 videos recorded by 35 modern smartphones. The videos on which we conduct experiments are not only the original native ones; we also use those compressed by the WhatsApp and YouTube algorithms so as to explore the effects of data recompression and to investigate challenging scenarios in which the training and testing datasets do not share common characteristics.To provide a baseline strategy for comparing the achieved results, we investigate the mono-modal attribution problems as well. Indeed, the vast majority of state-of-the-art works in multimedia forensics always deal with video sequences by only exploiting their visual or audio content in a separate way ,27,28,29Our work is organized as follows. In Identifying the camera model used to acquire an image or a video frame is possible, thanks to the many peculiar traces left on them at the shooting time. To better understand the traces that we are referring to, in this section, we provide the reader with some background on the generic acquisition pipeline of digital images. Then, since we investigate also the audio content of video sequences, we introduce the definition of the Mel scale and LMS of digital audio signals. In particular, the LMS is a very powerful tool for analyzing the spectral and temporal evolution of an audio track.Whenever we take a photograph with a digital camera or smartphone, we trigger an elaborate process consisting of several operations. This process, which lasts a few fractions of a second, starts when we press the shutter button and ends when we can visualize the shot taken. In general, the acquisition pipeline of a digital image is not unique. There can be differences among the vendors, the device models and the on-board technologies that are available. Nonetheless, we can reasonably model the image acquisition pipeline as a series of common steps , as depiLight rays hit a lens that focuses them on the sensor . The surAfter that, we have a processing phase consisting of additional operations. For instance, as lenses may introduce various kinds of optical aberrations , camera vendors typically apply some digital correction; this may introduce forensic traces. Furthermore, other common operations that are vendor-specific are the white balancing and the color correction. Eventually, a step of image compression is typically applied. In this regard, JPEG compression is the most widespread operation and again introduces implementation-specific and quality degrees of freedom.The Mel scale is a perceptual scale of pitches proposed in 1940 by . In partK triangular filters, where each filter has a maximum response at the center frequency and decreases linearly toward 0 until it reaches the center frequency of the two adjacent ones. Specifically, the filter centered around the pitch p in Mel scale can be modeled as follows:The human ear’s behavior can be simulated with the so-called Mel filterbank, a set of K distinct pitches), and rows are associated with frequencies.The entire Mel filterbank can be modeled as a two-dimensional matrix T temporal samples and F frequency bins, LMS can be represented as a 2D matrix By applying the Mel filterbank ocessing ,37,38. CThe problem we address in this paper is that of camera model identification on video sequences. We mainly focus on identifying the source camera model of digital video sequences, as the analysis of digital images has been widely addressed in the forensic literature, with excellent results ,18,21,22The problem of mono-modal camera model identification consists of detecting the device model used to acquire a specific kind of media at a single modality, for instance, given a photograph, understanding the model of the camera used to take it, or, alternatively, given an audio recording, detecting the used recorder model. Given a video, which is the case of our interest, the mono-modal model attribution consists of identifying the device model that shot it, using only the visual or audio information of the video itself.Given a video sequence, the problem of multi-modal camera model identification converts to identifying the device model that shot it, using both the visual and audio information of the video itself. In our case, we consider a closed-set identification, which consists of detecting the camera model used to shoot a video sequence within a set of known devices. In this scenario, the investigator assumes that the video being analyzed is taken with a device belonging to a family of devices that she/he knows. If the video does not come from any of those devices, the investigator will wrongly attribute the video to one of those.Content extraction and pre-processing: extraction of visual and audio content from the video sequence under analysis and manipulation of data prior to feeding them to CNNs;CNN processing: feature extraction and classification block composed of one or multiple CNNs.In this paper, we propose to solve the problem of closed-set multi-modal camera model identification on video sequences. In the following lines, we enter more in detail for each step of the proposed pipeline.The extraction and pre-processing phase consists of visual and audio content manipulation and data normalization.Extraction of Random extraction of Patch normalization in order to have zero mean and unitary variance as is commonly done prior to feeding data to CNNs.Considering the extraction and pre-processing of visual content from the video under analysis, this phase consists of three steps see :ExtractiExtraction of the LMS ocessing ,39,40,41Random extraction of Patch normalization in order to have zero mean and unitary variance, as previously described for visual patches.Regarding the audio content of the video under analysis, the extraction and pre-processing phase consists of three steps as well see :ExtractiIn the CNN processing step, the extracted pre-processed content is fed to one or multiple CNNs to extract distinguishable features among the different source camera models and classify the original one.M, where each node is associated with a particular camera model. The output value is an M-element vector defined as The mono-modal camera identification problem can be solved by feeding the visual or audio information extracted as shown in Late Fusion methodology: compare the classification scores of visual and audio patches, separately obtained from two single-input CNNs;Early Fusion methodology: build one multi-input CNN, feed this with both visual and audio content and exploit it to produce a single classification score.Considering multi-modal camera model identification, which is our actual task, we propose two distinct methods to solve the problem:In both proposed methods, we always provide pairs of patches as input to the network(s), composed of one visual patch and one audio patch extracted from the same video sequence under analysis.Separately feed a CNN with a visual patch and a CNN with an audio patch;Extract the classification scores associated with the two patches. In particular, we define m-th element of the score vector Select the classification score vector  separately. More specifically, the network working with visual patches updates its weights by optimizing the classification problem on the scores returned by In the second method, defined as Early Fusion, we build a multi-input CNN by joining together two CNNs. The union is made by concatenating the final fully-connected layers of the two networks and by adding three fully-connected layers up to the prediction of the camera model . The CNNs we use to solve the problem are the EfficientNetB0  and the The EfficientNetB0 belongs to the recently proposed EfficientNet family of CNN models , which hWe use EfficientNetB0 for processing visual patches; audio patches can be processed by means of both EfficientNetB0 and VGGish. To solve the proposed multi-modal camera model identification problem, we make some modifications to the network architectures in order to match the input audio data. In particular, to correctly process audio patches, we add an extra convolutional layer at the beginning of EfficientNetB0. We need this additional layer because EfficientNetB0 accepts three-channel patches as input . The extra layer applies a 2D convolution using In this section, we first present the dataset we work with, and the experimental setup . Then, we report the evaluation metrics and comment on the achieved results.We select video sequences from the Vision dataset , a recenFor what concerns the visual content of videos, we extract 50 frames per video sequence, equally distant in time and distributed over its entire duration. Then, we extract 10 patches per frame (taken in random positions), for a total of As for the audio, we extract the LMS based on the default parameters purposely designed for the VGGish network . The invConfiguration EV, which uses EfficientNetB0 for processing visual patches and VGGish for audio patches, considering the default audio frequency range required by VGGish ;Configuration EEConfiguration EEAs reported in Following a common procedure applied in CNN training, we initialize the EfficientNetB0 weights, using those trained on ImageNet database , while w® Xeon E5-2687W v4 (48 Cores @3 GHz), RAM 252 GB, one TITAN V (5120 CUDA Cores @1455 MHz), 12 GB, running Ubuntu 20.04.2. We resort to Pytorch [Concerning the dataset split policy, we always keep  Pytorch  as the Di, j) represents the probability that a patch of a video recorded by the i-th model is classified as a patch of a video recorded by the j-th model. The more effective the method, the more the confusion matrix tends to be diagonal. In particular, we evaluate results by means of the achieved balanced classification accuracy. These metrics can be computed as the average of the values lying on the confusion matrix diagonal.To evaluate the goodness of the system in classifying video sequences we use confusion matrices, where rows and columns are associated with the smartphone models under analysis. The value at position  to classify the query video; in the Early Fusion method, we build one multi-input network and feed it with visual/audio patch pairs extracted from the query video. For each methodology, we compare three different networks’ configurations, exploiting distinct architectures and data pre-processing.We evaluate our experimental campaign over video sequences selected from the Vision dataset. The videos on which we experiment are not only the original native ones, i.e., those directly acquired by the smartphone camera. We also use videos compressed by the WhatsApp and YouTube algorithms so as to explore many different training and testing configurations as well as to simulate realistic scenarios in which we have to classify data compressed through internet services . Moreover, we compare our proposed multi-modal methodologies with a mono-modal attribution strategy selected as the baseline.The achieved results show that the proposed multi-modal methods are significantly more effective than standard mono-modal methods; on average, the Late Fusion approach reports the best results. In general, we can correctly identify native and YouTube video sequences with accuracy of up to


N = 142) played a video game using sexualized or non-sexualized female characters. Cognitive load was manipulated by setting the difficulty level of the game to low or high. After gameplay, participants read a rape date story, and were then asked to judge the victim’s and the perpetrator’s degree of responsibility and humanness. Based on the General Aggression Model (GAM), it was hypothesized that playing the video game with a sexualized content would increase the responsibility assigned to the victim and diminish the responsibility assigned to the perpetrator. Further, degree of humanness of the victim and the perpetrator was expected to mediate this relation. The results were partially consistent with these predictions: Playing a video game containing sexualized female characters increased rape victim blame when cognitive load was high, but did not predict degree of humanness accorded to the victim. Concerning the perpetrator, video game sexualization did not influence responsibility, but partly influenced humanness. This study concludes that video games impact on attitudes toward women and this, in part, due to its interactive nature.The potential negative impact of sexualized video games on attitudes toward women is an important issue. Studies that have examined this issue are rare and contain a number of limitations. Therefore, it largely remains unclear whether sexualized video games can have an impact on attitudes toward women. This study examined the consequences of sexualized video game content and cognitive load (moderator) on rape victim blame and rape perpetrator blame (used as a proxy of rape myth acceptance), and whether the degree of humanness of the victim and of the perpetrator mediated these effects. Participants ( Despite the increase attention to violence against women through movements such as “#MeeToo” or Amnesty’s “No is no” campaign, sexual violence remains a serious problem . It is rIn this study, we focused on video games as one possible contributing factor to the prevelance and believability of rape myths  for several reasons. Video games are a widespread activity among people of all ages, including adults. For example, in Europe, more than half of adults play video games, which equals to some 250 million gamers in the EU . It shouIn order to explain the impact of sexualized video game content on the attitude toward women, the present study was based on the General Aggression Model GAM; . InitialDegree of humanness accorded to the victim and the perpetrator was chosen because, as mentioned previously, in most video games, female characters are sexualized and objectified, which leads to dehumanization . DehumanAnother important variable to take into account is the availability of cognitive resources. According to the GAM, the interaction of personal and situational variables leads to the immediate appraisal of a situation  and therefore to the most spontaneous and the most accessibly linked thoughts and feelings. As presented previously, concerning a testimony of rape, these thoughts can lead to the dehumanization of the victim and to the adherence to RMA. The situation can be reappraised, but in order to do this, the person needs sufficient cognitive resources. The expenditure of cognitive resources is an inherent feature of video games. Playing video games requires more cognitive resources than other types of media because the player needs to concentrate on, and interact with, the media . For thiWe also included several covariates. Based on the GAM, we know that aggressive behaviors will arise, not only from the effect of situational variables, but from their interaction with personal variables. For this reason, it seems obvious to include as covariates not only a measure of aggressive personality as proposed by the GAM, but also a measure of benevolent sexism. Individuals who score high on benevolent sexism strongly endorse the belief that women are pure and should be protected. However, such a belief implies that women must behave in ways that allow them to be protectable . If womeIn view of the above facts, the objective of the present study is to examine: (1) the impact of sexualized content of video games on state RMA, (2) the potential moderating effect of cognitive load, and (3) the mediating effect of humanness. Measures of trait aggression, benevolent sexism and gamer identification where included as covariates. These issues will be examined while at the same time addressing the limitations of previous studies and introducing new innovations. In fact, the mediating effect of humanness (of both the victim and the perpetrator) concerning the relation between sexualized content of video games and RMA will be examined, and this to the best of our knowledge for the first time in the context of a study on sexualized video games. Furthermore, studies examining the effects of sexualized video game content on RMA have resulted in mixed results, probably due to some limitations concerning the methodology. One study showed that sexualized content of video games can directly increase RMA , and anoTo conclude, our hypotheses were: (1a) when exposed to a sexualized video game, participants will score high on the state RMA measure and thus will hold the victim (and not the perpetrator) partially or fully responsible for being raped (rape victim blame), especially in conditions of high cognitive load; (1b) degree of humanness will mediate the relation between sexualization and rape victim blame: the sexualized condition will decrease the human uniqueness score and the human nature score attributed to the victim, which will increase rape victim blame; (2a) when exposed to a sexualized video game, rape perpetrator blame will diminish, especially in conditions of high cognitive load; and (2b) degree of humanness will mediate the relation between sexualization and rape perpetrator blame: the sexualized condition will increase the human uniqueness score and the human nature score attributed to the perpetrator, which will diminish perpetrator blame.M = 21.66, SD = 1.53) recruited from a Belgian university, through social networks and by word of mouth. Participation was voluntary and unpaid. Among the 142 participants, 65 identified themselves as video game players. They reported spending between 1 and 20 h per week playing video games .Participants were 142 university students  aged between 18 and 27 years . In this fighting game, the player embodies a character who fights with punches and kicks against another (single) opponent who counterattacks. Different keys on the computer keyboard allow participants to perform punches, kicks, jumps, crouches and to combine different moves. The goal is to exhaust the opponent’s health meter or have more health than the opponent when the time runs out. This game was chosen as it offers the possibility to select female characters, but also because it offers the possibility to choose between very covering clothes and very exposing clothes . Equally important, this game makes it easy to decide the level of difficulty and allows participants to play without having to familiarize themselves with complicated rules (the only required knowledge is how the keyboard keys are related to various movements). Two variables were manipulated in this study: sexualization and cognitive load. Sexualization was manipulated by changing the outfit of the two female characters . In the highly sexualized condition, both characters wore a revealing swimsuit (the characters were completely naked except for the breasts and gender), whereas in the non-sexualized condition, both characters were fully clothed (only the face and hands were visible). Cognitive load was manipulated by modifying the action of the computerized opponent. In the low cognitive load condition, the opponent was programmed to never fight back, whereas in the high cognitive load condition, the opponent was programmed to fight back. In both conditions, participants were asked to learn the action produced by each button and their combination.very low mental effort) to 9 . When answering, participants were asked to only consider the mental effort provoked by the video game and not to consider the mental effort provoked by the secondary task.Pre-test participants (who were not included in the main study) were recruited via social networks. Both cognitive load conditions were pre-tested using a dual-task methodology . We usedN = 10) or the low cognitive load condition (N = 9). As expected, participants in the high cognitive load condition, compared to participants in low cognitive load condition, committed significantly more omissions during the verbal 2-back task  and rated the mental effort provoked by the video game as higher .Nineteen participants were randomly assigned to either the high cognitive load condition . Originally this scenario was separated into two parts , one in Participants reported their gender and age. They also reported the average number of hours they spend playing video games each week, and their degree of familiarity with the video game used in the study . Familiarity has been added as a covariate.trongly agree to 5 = Strongly disagree. Internal consistency was excellent (α = 0.91).Identification was measured via a set of 4 questions: “I see myself as a gamer,” “I like being a gamer,” “I feel a strong connection with other gamers” and “I identify with other gamers.” Participants rated the statements on a scale ranging from 1 = SNot at all to 7 = Completely or totally. These measures, representing the dependent variable, were used as state measures of rape myth acceptance. A high score on this measure for the assessment of the female character indicates a strong acceptance of rape myth, while a high score on the male character assessment indicates a weak acceptance of rape myth. Internal consistency in this study was good for both victim and perpetrator blames .Victim and perpetrator blame were assessed using four items issued from a previous study on the same topic . The iteStrongly disagree to 7 = Strongly agree. The humanness score was therefore composed of two distinct scores: human uniqueness (the average of positive and negative human uniqueness scores) and human nature (the average of positive and negative human nature scores).To assess humanness of the victim and of the perpetrator, participants were given a list of 20 traits, taken from Not at all like me to 6 = Completely like me (Cronbach α = 0.71).Finally, participants completed a French version  of the AParticipants also completed the 11-item benevolent sexism subscale  of the AStrongly Agree to 5 = Strongly Disagree. The French version possesses good levels of internal consistency (α = 0.89).The Updated Illinois Rape Myth Acceptance Scale  was usedUpon arrival, participants gave their informed written consent and were told that they would participate in a study about the impact of various types of media on the judgments about agentivity (capacity of action). Participants were told that they would be exposed to stories through various types of media and that they would thereafter be asked to judge the degree of agentivity of each protagonist in the story. First, they completed questions about demographics and their identification as a gamer. Based on the procedure used by N = 200), resulting in a final sample of 142 participants.After that, participants completed the Aggression Questionnaire, the Benevolent Sexism scale and the Illinois Rape Myth Acceptance Scale, and in that order. A debriefing followed, which included a check for suspicion. Each participant who guessed the real purpose of the study was removed from the sample. Based on this criterion, 58 participants (29%) were removed from the initial sample  and trait rape myth acceptance . Human uniqueness was predicted by gamer identification  and familiarity with the game . Human nature was predicted by benevolent sexism . Further, the first model suggested a relation between sexualization and victim blame that is mediated by the degree of humanness of the victim. None of the indirect paths were significant.In the first model, cognitive load was examined as a moderator of the relation between sexualization and victim blame, and as a moderator of the relation between sexualization and degree of humanness attributed to the victim . The onlb = 0.187, t = 2.20, SE = 0.085, p = 0.029) and trait rape myth acceptance .In the second model, cognitive load was examined as a moderator of the relation between sexualization and perpetrator blame, and as a moderator of the relation between sexualization and degree of humanness attributed to the perpetrator . None ofThis study examined the impact of sexualized video game content on rape victim and on perpetrator blame. Only one other study has previously examined the moderating effect of cognitive load . In addiConsistent with the first hypothesis (1a), victim blame was significantly more likely to occur when the participant was exposed to a sexualized video game, but only in conditions of high cognitive load. The second hypothesis (1b) was not supported. The impact of sexualized video game content on victim blame was not mediated by humanness. Further, the third hypothesis (2a) and the fourth hypothesis (2b) were not supported. Results did not show a direct effect of sexualized content on perpetrator blame and cognitive load did not moderate this relation. Moreover, the impact on sexualized video game content on perpetrator blame was not mediated by humanness.Cognitive load seems to be an important variable to account for as it appears to moderate the impact of exposure to sexualized video game content on victim blame. We could only find one previous study that measured cognitive load when examining the impact of sexualized video game content on RMA  and resuIn addition, the present study is the first to examine the impact of sexualized video game content on perpetrator blame. The results did not confirm our third hypothesis. Indeed, neither a direct impact of sexualized game content nor a moderated effect by cognitive load was found on perpetrator blame. To the best of our knowledge, only one study has previously examined the impact of sexual objectification on perpetrator blame . This stWe also assumed that the impact of sexualization on victim/perpetrator blame was mediated by the degree of humanness accorded to the victim/perpetrator (second and fourth hypotheses). These hypotheses were not supported. Regarding the second hypothesis, humanness did not mediate the impact of sexualized content on victim blame. Moreover, no direct effect of sexualization on humanness was observed. These results are not in line with previous studies on the dehumanization of sexualized women. In these studies , when a It would be important to investigate how one feels about the responsibility attributed to the victim and the abuser, given that this can be perceived very differently. As explained by the GAM, if an individual has the necessary cognitive resources s/he will be able to re-evaluate a situation and thus go against her/his first stereotyped thoughts and feelings. While it is true that regular video game players increase their expertise and thus could free up cognitive resources that can allow them to adequately assess the situation, we also know that the difficulty of video games increases with each level. As is often the case, the solution will probably lie more in the representation of female characters, which can be portrayed in a less sexualized manner, rather than in the game itself and the related degree of cognitive load.In general, the results are consistent with the General Aggression Model GAM, . The sitOne main contribution of these results is that it confirms the particular role of cognitive load caused by video games. In this study, cognitive load was directly caused by features from the video game . However, it remains unclear which particular characteristics of the video game lead to high cognitive load. For example, future studies could test if high cognitive load results from features such as interactivity , reactivity , or the degree of immersion in the video game . Further, it would be important to manipulate some of these characteristics in other types of media  to test if the impact is similar to video games. Also, it would be interesting to include, in addition to the subjective gamer identification measure, an objective measure of the degree of experience of the video game player. If high cognitive load is only determined by the difficulty (level) of the game itself, and not by features such as interactivity, reactivity, or degree of immersion, then experienced and novice players might be affected in different ways. Thus, future studies should focus on this variable and recruit enough video game players to allow a comparison between groups of novice, intermediate, and experienced players, while taking into account the type of games usually played by each participant.The present study has some limitations. The video game that was chosen for the present study was not ideal. Since we wanted to have as high a level of ecological validity as possible, we wished to include a video game that people actually use. In addition, we needed to find a video game that included female characters and that enables a manipulation of both degree of cognitive effort and degree of sexualization. The only video game at the time of the study that answered to all these criteria was Ultra Street Fighter IV. However, cognitive load may have been confounded with aggression. Indeed, in the high cognitive load condition, the opposing character defends himself, which is not the case in the low cognitive load condition. Furthermore, degree of sexualization was manipulated by modifying outfits of the female video game characters. Given that Ultra Street Fighter IV is a fighting video game, the female characters possess features that are stereotypically inconsistent with the usual submissive role of female characters in video games. For instance, it was easy to see the strong muscles of the sexualized characters. Such stereotype-inconsistent information might also act as a confounding variable and might prime other concepts such as agentivity, which is known to mediate the relation between sexualized game content and risk of sexual aggression . This agIn conclusion, the present results contribute to a better understanding of the impact of sexualized video game characters on rape victim blame. Our results show that playing a video game containing sexualized female characters increases rape victim blame, when participants’ cognitive resources are low. Negative attitudes toward women in general, and rape myth attitudes in particular, are an important issue in our society and one of its underlying causes might be the sexualized content of video games.https://doi.org/10.18710/FDJEIB.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The study involving human participants were reviewed and approved by the Comité Éthique de la FPLS, Université de Liège, Belgique. The participants provided their written informed consent to participate in this study.TN, FL, and JB contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Anti-immigration rhetoric in the mass media has intensified over the last two decades, potentially decreasing prosocial behavior and increasing outgroup hostility toward immigrants, and fostering ingroup favoritism toward natives. We aim to understand the effects of negative and positive discourses about immigration on prosociality at different levels of societal ethnic diversity. In two studies , we conduct a survey and a 3X3 between-subject experiment, including money-incentivized behavioral games measuring prosociality. We manipulate media representations of immigrants and the probability of interacting with immigrants (the latter measuring diversity). Results show that negative news affects prosociality as a function of the probability of interacting with immigrants. Negative portrayals increase altruism and trustworthiness in ethnically homogenous settings relative to unknown and ethnically-mixed contexts. These results are stronger for right-wing and high-prejudice respondents. Moreover, negative media portrayals of immigrants increase the testosterone-cortisol ratio, which is a proxy for proneness to social aggression. Negative news also increases outgroup-related perceived health risk, outgroup anxiety and outgroup threat less in ethnically-homogeneous contexts. Overall, negative portrayals of immigrants generate physiological and emotional hostility toward the outgroup, and ingroup favoritism in economic transactions, possibly determining efficiency losses in ethnically-diverse markets, relative to ethnically-homogeneous markets. As a result, both traditional and social media—the latter often relying on unverified sources and socially provocative content3—have frequently framed immigrant people as a threat1. During the COVID-19 pandemic, anti-migrant rhetoric even made migrants—and sometimes foreign nationals more generally—into COVID-19 carriers4.In the last two decades, political anti-immigration rhetoric has intensified in Western European countries and in the United States. Disputes over immigration issues  have also become more frequent6 and the likelihood of anti-immigrant party voting7. Conversely, individuals exposed to news about immigration with politically-correct language tend to support less restrictive immigration and border security policies8. These phenomena can, from a social psychological perspective, be understood in the light of intergroup relations and intergroup conflict. A negative narrative on immigration fosters outgroup threat (from immigrants), and makes the differences and boundaries between the ingroup (natives) and the outgroup (immigrants) salient9. This strengthens ingroup identity and generates forms of ingroup favoritism, together with prejudice and discrimination toward the outgroup10.Negative depictions of immigration in public and media discourse increase prejudice and mistrust toward immigrants11. In trust games natives reciprocate, on average, the trust of immigrants less frequently than the trust of natives; this reciprocity gap increases if natives have negative attitudes toward ethnic diversity and immigration12. Low levels of trust, reciprocity, and altruism—that is, low levels of prosociality—undermine cooperation and the efficiency of economic exchanges14. As a result, anti-migrant rhetoric may have an unpredicted, perhaps unwanted, adverse economic impact. More specifically, negative media representations of immigrants may result in hostility and lower levels of prosociality in transactions with immigrants6; but negative media representations of immigrants may also result in higher levels of prosociality toward natives, due to ingroup favoritism or similar phenomena, such as parochial altruism15. Positive media representations of immigrants may, conversely, foster prosocial behavior toward immigrant transaction partners. Observing positively portrayed outgroup members in the media is a form of indirect positive intergroup contact16 and it is associated with more favorable attitudes toward the outgroup5. Therefore, positive portrayals of immigrants in the mass media could help close the immigrants-natives reciprocity gap found in previous research12.Besides increasing prejudice and support for anti-immigrant leaders and policies, negative portrayals of immigrants in the mass media can heighten discrimination in socio-economic transactions. This includes buyer–seller, borrower–lender, or employer–employee interactions17. Thus, when native people are exposed to negative portrayals of immigrants, their prosociality is likely to be lower in the case of markets or societies with medium or high ethnic diversity. This could be caused by both outgroup hostility6 and by the fact that ethnic diversity increases the uncertainty regarding shared social norms, including those related to prosociality18. Conversely, negative media representations of immigrants could increase prosociality when the probability of interaction with immigrants is low or close to zero , due to ingroup favoritism10. Positive media representations of immigrants may instead attenuate the negative relationship between societal ethnic diversity and prosociality17. If no information on potential transaction partners is available, a ‘veil of ignorance’ could promote more impartial decision-making19, helping to put aside mass-mediated portrayals of immigrants. Alternatively, people could fall back on their feelings and expectations on prospective transaction partners. If this is the case, the combination of negative media representations of immigrants and no information on transaction partners may raise suspicion and worry, with, consequently, decreased prosociality.However, partners are known only in fully informed interactions. Most of the time, individuals move through various markets characterized by imperfect information and incomplete contracts. In these contexts, natives can only estimate the probability of interaction with immigrants based on the available information on the ethnic diversity of these markets. There is a robust negative relationship between ethnic diversity and social trust, especially in local contexts7, and that people holding negative attitudes toward ethnic diversity are less prosocial toward immigrants12. Moreover, there is a robust negative association between ethnic diversity and social trust17. This paper investigates—as far as we know for the first time—the combined effect of media representations of immigrants and the level of ethnic diversity in a society or in a market. An endemic issue with non-experimental research in this area is that individuals choose media content and transaction partners. As such, confounding factors may impede the identification of the causal effects that media portrayals of immigrants have on prosociality. Confounding factors may also obscure how such causal effects change in terms of the ethnic diversity of expected interactions. By exogenously varying representations of immigration and the probability of being matched with immigrant partners, this paper sheds lights on how these two factors affect human prosociality in causal terms. This test is important because if negative portrayals of immigrants reduce prosociality in ethnically-diverse societies, they will also generate unexpected negative externalities for natives belonging to those societies. Moreover, negative portrayals of immigrants may increase prosociality in ethnically homogeneous contexts. If such effects are verified, in the long term anti-migrant rhetoric will produce a comparative disadvantage for ethnically-mixed societies.This paper aims at understanding the consequences of anti- or pro-immigration rhetoric in the media on prosocial economic behavior at different levels of societal ethnic diversity, depending on the probability of interaction between immigrants and natives. We also investigate the mechanisms and possible heterogeneity of these effects. Previous research has showed that negative portrayals of immigrants in the mass media increase prejudiceItalian list; 50% Italian names and 50% names suggesting immigration background and mainly African origin, mixed list ; ‘veil of ignorance’, no list. Then, respondents play money-incentivized behavioral games measuring altruism , trust and trustworthiness .In two studies , we investigate the effects of media portrayals of immigrants and the ethnic diversity of potential transaction partners on prosocial choices . We do so by using money-incentivized behavioral games. The list of potential transaction partners includes: immigrant and native names, or only native names, or unknown-origin names. We implement a 3 × 3 between-subject experiment, manipulating: (i) a 3-min television report portraying immigrants in a negative vs. positive way with no video (control condition); ii) the names of possible transaction partners: distinctively Italian names, 20 and intergroup anxiety21 related to immigrants, and through the growth rate (before vs. after the video) of salivary testosterone-cortisol ratio, a proxy for proneness to social aggression22. These measures allow us to shed light on the mechanisms underlying the relationship between media portrayals of immigrants and prosocial behavior.Study 1 additionally explores the emotional and physiological reactions activated by the video. This is done through self-report measures of perceived outgroup threat23 and immigration attitudes . Importantly, in Study 2 we also test the presence of heterogeneity in treatment effects: more specifically, we investigate whether the combined effects of video and lists on prosocial choices are stronger for respondents with certain political orientations and attitudes toward immigrants. Moreover, in Study 2 we employ as outcome variables intergroup anxiety, perceived outgroup threat, and outgroup-related perceived health risk (during a pandemic). Only in Study 2 are outgroup threat and intergroup anxiety measured after both video and list manipulations.Study 2 replicates analyses in a nationally representative sample, and controls for a large set of individual characteristics, including pre-treatment self-report measures of social preferencesAltruism and trust were measured in terms of the amount of money sent to the partner divided by the initial endowment, respectively in the DG and in the TG. Trustworthiness was measured in the TG and its models were conducted in the long-format dataset: trustees’ return decisions were distinct observations, nested within individuals (strategy method). Trustworthiness was computed as the amount returned divided by the hypothetical trustor’s transfer, multiplied by three and divided by 100 (to have a standardized measure).We performed linear models with altruism, trust and trustworthiness predicted by age, gender, all conditions and their interactions . In all models, we employed robust standard errors (Huber and White correction); in the models for trustworthiness, standard errors were additionally clustered at the individual level to account for repeated choices within trustees. Full tables are reported in Supplementary Materials p = 0.058), and the interaction between negative video and Italian list was positive ; apart from gender , all other coefficients were not statistically significant (at p < 0.10). Pairwise comparisons (contrasts) of estimated marginal means  and no list  conditions.Results showed that altruism was negatively associated with negative video  and positively associated with the interaction between negative video and Italian list ; all other coefficients were not statistically significant, except the hypothetical trustor’s transfer . Pairwise comparisons of estimated marginal means  condition. This is consistent with results on altruism. These results are from estimates employing the long-format version of the data, exploiting the full set of hypothetical strategies of the trustee and controlling for the trustor’s hypothetical transfers (see SM1).Trustworthiness was negatively associated with the negative video condition  showed that in both models, none of the contrasts  were statistically significant. These results, combined with the ones reported in Fig. p = 0.003), while no statistically significant effects of the positive video were found; no statistically significant effects were found for intergroup anxiety . Results were the same when anxiety and threat were measured by averaging items of each scale. In the physiological model  by 31 percentage points compared to positive video (p = 0.001) and 39 percentage points compared to no video (p < 0.001).To shed light on the emotional and physiological mechanisms activated by the video, we performed three linear models: intergroup anxiety, perceived outgroup threat , and growth in testosterone-cortisol ratio were predicted by age, gender and video conditions . The negative video was associated with increased perceived outgroup threat relative to no video , duration of the survey, and pre-treatment social preferences or gift propensity. Full tables are reported in Supplementary Materials p = 0.015); no other coefficients were statistically significant, except social preferences  and the hypothetical trustor’s transfer . Pairwise comparisons of estimated marginal means  and no list  conditions. This was consistent with the results from Study 1.Results only partially confirmed the behavioral effects found in Study 1. More specifically, trustworthiness was positively associated with the interaction between the negative video and Italian list , and positively associated with the interaction between the negative video and the Italian list ; no significant treatment effects on trustworthiness were found for left-wing respondents. A similar pattern emerged for trust: right-wing respondents’ trust was positively related to the interaction between the negative video and the Italian list , whereas the conditions had no statistically significant effects on the trust of left-wing respondents. As far as blatant and subtle prejudice toward immigrants were concerned, results showed that the combined effect of the negative video and the Italian list on trustworthiness emerged only for respondents with levels of prejudice above the median. In fact, trustworthiness was positively associated with the interaction between negative video and Italian list at high levels of blatant prejudice  and subtle prejudice . On the other hand, no significant treatment effects on trustworthiness were found for respondents with low levels of prejudice toward immigrants.To investigate the possible treatment heterogeneity of video and list conditions, linear models for altruism, trust and trustworthiness were performed again in sub-samples based on the respondent’s self-reported political orientation , and on the respondent’s level of pre-treatment blatant and subtle prejudice toward immigrants. With political orientation, results showed that the combined effect of the negative video and the Italian list on trustworthiness was there for right-wing, but not for left-wing respondents. For right-wing respondents, trustworthiness was negatively associated with the negative video , but negatively associated with the interaction between the negative video and the Italian list . Among controls, pre-treatment immigration attitudes significantly predicted all outcomes . Pairwise comparisons of estimated marginal means  as outcome variables. In these models, we also controlled for pre-treatment anti-immigration attitudes . Results showed that these two variables were positively associated with the negative video . It was also negatively associated with the interaction between the negative video and Italian list conditions . Pre-treatment immigration attitudes were the only statistically significant predictor .Study 2 was carried out in the year of the COVID-19 outbreak, during a period with relatively low levels of mortality and infections. To test whether immigrant portrayals also played a role in perceived health risk, we performed again the baseline linear model (controlling for pre-treatment immigration attitudes). We did so by using as outcome variable participants’ self-reported belief that immigrants represent a health risk for the population during a pandemic (asked after manipulations). Despite the potential health risks from immigration not being discussed in the video, which was prepared before the COVID-19 outbreak, perceived health risk was positively associated with the negative video condition  and the awareness of being in an ethnically homogeneous society  could have made ingroup identity more salient, thereby waking up the “us and them” dichotomy that is not salient in the two conditions taken separately . According to social identity theory, a minimal sense of ingroup identity is enough to foster ingroup favoritism3.Lastly, heterogeneous treatment effects emerged: respondents who declared themselves more right-wing, or who endorsed higher levels of prejudice toward immigrants, were more prosocial only toward Italian transaction partners in response to negative portrayals of immigrants. On the contrary, the ingroup favoritism pattern was not present in left-wing and low-prejudice respondents. Therefore, mass-mediated anti-immigration rhetoric might have more influence on people who already hold negative expectations and beliefs in immigrants. This results in an extreme polarization of opinions, and possibly an echo-chamber effectWe acknowledge that the results of the two studies are not fully consistent. We have consistency across the two samples for three of the five outcomes . However, participants in Study 2 seem less sensitive to video priming than participants (students) in Study 1. This could be due to several factors: (i) participants in Study 1 were more used to negative representations of immigrants because at the time of Study 2 (August 2020) there was much anti-immigration rhetoric in Italy; (ii) the negative video presented immigration as an economic, social and cultural problem, whereas at the time of Study 2 more salient themes related to immigrants were their free circulation and the associated health (COVID-19) consequences for Italians; (iii) unlike the participants in Study 1, respondents in Study 2 did not go through tasks involving the measurement of physiological parameters, which could have increased the stress and reactivity to stimuli in the participants of Study 1; (iv) as Study 2 was carried out in an uncontrolled environment, participants in that study might have gotten distracted more often while watching the video relative to students in a laboratory, in a separate workstation, isolated from any external stimulus. Another possible explanation to the partial consistency of the results is that the statistical power may not be enough to detect small behavioral effects, especially for interaction terms. Future studies could replicate our experiments in larger samples.Interestingly, no significant effects were found for the mixed list condition. A possible explanation is that participants in the mixed list condition might have underestimated the probability of being matched with an immigrant—instead of a native—player. This explanation is perhaps particularly plausible in Study 1, in which participants saw other (native) participants in their lab session. Moreover, participants in the negative video condition underestimated the likelihood of being matched with an immigrant player more than participants in the other video conditions  and “America first” within the anti-immigration campaigns of the last decade. Such campaigns can simultaneously generate negative emotional reactions against migrants and higher preference for, or greater ease associated with, native individuals.This paper shed light on the socio-economic, emotional and physiological consequences of negative portrayals of immigrants in societies with varying degrees of ethnic diversity. Future research could usefully expand the understanding of the socio-economic consequences of xenophobic narratives in ethnically-diverse societies, testing the role of diverse information sources and of factors counteracting the behavioral and economic effects of biased news on immigration.https://osf.io/jwv5a/. Full tables are available in SM1; additional analyses are reported in SM2.Games instructions, the on-line questionnaire, and details on physiological measures can be found at 30. Considering the main effects of our predictors and the interactions between them, the required sample was 196 participants to observe a medium effect size  of both interactions and main effects. All analyses were performed with the R31 packages estimatr32 and emmeans33.The entire research was approved by the Ethical Committee of the University of Turin (protocol #55474). All participants delivered their informed consent before participating. All experiments were performed in accordance with relevant guidelines and regulations. A de-briefing session was carried out at the end of each study to clarify the aims of the research and the ad-hoc nature of the video. Before the data collection of Study 1, we performed power analyses with G*Power 3.1Individuals (N = 384) were randomly extracted from 3942 students registered on the University on-line recruitment platform and assigned to 16 lab sessions. Participants who showed up  gave their informed consent and were randomly assigned to watching the negative television report, the positive television report, or nothing (control condition). Watching the video was the very first part of the experiment. The negative television report highlighted several negative aspects of immigration, such as its socio-economic cost and the threat to the cultural identity of natives. Conversely, the positive television report emphasized the positive aspects associated to immigration: in this case, immigrants were portrayed as a socio-economic resource for the receiving community, and as an opportunity to enrich the local culture. The reports’ effectiveness was confirmed by manipulation checks and by a preceding pilot study (see SM2): participants correctly identified the valence of immigrant portrayals, and understood whether immigrants were being portrayed as a cost/threat or as a resource/opportunity.Participants were given two self-use oral swabs: the baseline sample was taken before the video, the second one after about 10 min (including video for non-control participants). After the video , and before the second saliva sample, respondents answered questions measuring (i) perceived outgroup threat  (ii) perceived intergroup anxiety ; (iii) other questions for another study with other research goals; and (iv) the perceived valence of the video.Then, participants entered the behavioral games phase. They were randomly assigned to one of the three list conditions for subsequent money-incentivized behavioral tasks. To avoid experimenter-demand effects and unrealistic conditions, we did not employ a full immigrant-names list. To avoid deception, we employed names and decisions of same-session participants for the Italian and for the no list conditions, and of both session participants and immigrants from previously implemented experiments for the mixed list.TG and DG appeared in randomized order. In the TG, the trustor was endowed with ten euros and decided how much to transfer to the trustee; the amount sent was tripled by the experimenter and was sent to the trustee, who decided how much to return to the trustor. Trustees made ten return decisions, one for each hypothetical trustor’s transfer (strategy method). In the DG, the sender was endowed with ten euros and decided how much to send to the receiver, who then took no action. Participants were informed about the structure of the games and payment before the tasks. They then played all roles playing both player roles in the TG and DG . They finally took decisions in the Risky Decision task aimed at eliciting preferences towards risk . Game partners’ identity and game-specific payoffs were not revealed. After the games, participants were paid according to their payoff in one randomly selected game role (out of four), with one randomly selected game partner. The maximum (average) payoff was 20 (11.96) euros, including five euros as show-up fee.For the sampling, programming, implementation and payments of Study 2, the polling company “Demetra” was contracted. A nationally representative  sample of Italian adults was recruited (N = 666) by the company and participated in an on-line survey lasting approximately 25 min. The sample only included native Italians; neither first- nor second-generation immigrants participated. Participants were randomly assigned to the same between-subject manipulations as in Study 1 (conditions: 3 video × 3 list).23; (iv) two validated scales measuring prejudice toward immigrants, more specifically subtle prejudice  and blatant prejudice24, which additionally included one item on perceived health risk associated with immigration ; (v) other questions for another study with other research goals; and (vi) an attention-check question.After general instructions and informed consent, respondents answered (i) socio-demographic and media usage questions; (ii) a political orientation question ; (iii) questions aimed at measuring gift propensity , social preferences , and risk propensity , all taken fromThen, participants watched one of the three videos (randomly assigned), and entered the behavioral games (as in Study 1) immediately afterwards. Before playing, participants read the game instructions and were then shown a list containing either only Italian names  or half Italian and half immigrant names (the Mixed list); control participants received no list. Subjects in the list conditions were told that they could be matched with one of the listed players. The Italian list contained 23  names selected from the most popular names in Italy. The Mixed list conditions had 11  names from the Italian list, plus 11  non-Italian names chosen among the most popular ones in the country of origin of refugees . Selected names were kept constant within the list conditions, thereby minimizing potential gender, nationality and similarity effects.After the games, participants answered a second attention-check question, and then to questions measuring: (i) the subjective probability of playing with an immigrant; (ii) perceived outgroup threat ; (iii) an item measuring the respondents’ belief that immigrants represent a health risk for the Italian population during a pandemic; (iv) intergroup anxiety ; (v) other questions for another study with other research goals; (vi) voting preferences and income. The survey ended with manipulation-check questions about the valence of the video and a debriefing screen, which informed participants that the matching with one of the other players in the list might not be consistent with the names shown in that list.7 euros; a participation fee was added by the company.Overall, 606 participants were paid at the end of the survey by the company, which excluded 60 observations that did not meet their minimum data-quality standards . The maximum (average) payoff was 15Compared to highly controlled lab-experiments, on-line surveys are more subject to contextual factors affecting data quality. For this reason, data analysis was carried out on a total of 535 cases after data cleaning, which was based on the elimination of individuals who, in the order: (i) failed one of the two attention checks (N = 29); (ii) stayed on the video page less than the time of the video duration, namely 3 min (N = 25); (iii) fell above the ninetieth percentile of the time distribution on the video page ; and (iv) took an implausibly short or long time to complete the survey considering the distribution of survey duration (N = 30), namely below the 2.5th percentile  and above the 97.5th  percentile of overall duration.Supplementary Information.


To examine changes in drinking behavior among United States (US) adults between March 10 and July 21, 2020, a critical period during the COVID‐19 pandemic.Longitudinal, internet‐based panel survey.The Understanding America Study (UAS), a nationally representative panel of US adults age 18 or older.A total of 4298 US adults who reported alcohol use.Changes in number of reported drinking days from March 11, 2020 through July 21, 2020 in the overall sample and stratified by sex, age, race/ethnicity, household structure, poverty status, and census region.Compared with March 11, the number of drinking days per week was significantly higher on April 1 by an average of 0.36 days , on May 1 by an average of 0.55 days , on June 1 by an average of 0.41 days , and on July 1 by an average of 0.39 days . Males, White participants, and older adults reported sustained increases in drinking days, whereas female participants and individuals living under the federal poverty line had attenuated drinking days in the latter part of the study period.Between March and mid‐July 2020, adults in the United States reported increases in the number of drinking days, with sustained increases observed among males, White participants, and older adults. The coronavirus COVID‐19) pandemic is an international emergency that has dramatically changed daily life. This global pandemic is expected to have lasting effects on individual well‐being including increased prevalence of psychological distress 9 pandemi, 3. The One possible implication of the COVID‐19 pandemic is changes in alcohol use in the general population. Alcohol use, including high‐risk drinking, has increased in the United States over the past decade, particularly among females, older adults, racial/ethnic minorities, sexual minorities, and individuals with lower income , highligCollectively, these findings suggest that there may be increases in alcohol consumption during the COVID‐19 pandemic, but this evidence has largely been limited to cross‐sectional and ecological analyses and it remains unclear whether there are sustained increases in use. Therefore, it is important to examine changes in drinking behavior over time and identify sociodemographic subgroups that may be especially at risk for adverse outcomes. To address this gap, the objectives of the current study were (i) to examine changes in number of drinking days from March 10, 2020 through July 21, 2020 among a nationally representative cohort of US adults who reported any alcohol use during the survey period, and (ii) to determine whether trajectories of drinking behavior differed among key sociodemographic subgroups.https://UASdata.usc.edu).Participants were drawn from the Understanding America Study (UAS), a probability‐based, nationally representative, internet panel of adults (18‐years and older). This study used data from nine waves of the UAS; the baseline wave was conducted from March 10, 2020 to March 31, 2020, and follow‐up waves were conducted thereafter at 2‐week intervals between April 1, 2020 and July 21, 2020. UAS participants were selected using address based sampling (ABS), in which postal records are used to select a random sample from a listing of residential addresses. The recruitment involves several steps, including prepaid and conditional incentives and several reminders. Potential participants without prior internet access are provided with tablets and broadband internet connections. Once respondents have joined the panel, they are surveyed via computer, mobile device, or tablet. Additional details regarding the UAS methodology can be found at the UAS website , we included only complete cases at each time point in our analyses, meaning that data were not missing for any of the identified variables. Altogether, 4298 unique participants were included; 62.2% completed nine surveys, 15.1% completed eight surveys, 7.2% completed seven surveys, and the remaining 15.5% completed between one and six surveys  to 7 . Number of reported drinking days was selected as the outcome of interest because this measure was consistently assessed at each wave during the study period.We used survey date as the time scale to assess changes over time. Survey date was entered into each model as a continuous variable representing the number of days since March 10, 2020, ending on July 21, 2020 . Given evidence of non‐linear changes in the number of drinking days over time, we modeled survey date with restricted cubic splines, which generate smoothed curves for the relationship between continuous exposures and outcomes. Cubic splines capture features that may be missed by traditional techniques such as linear models or categorization into bins . We geneSociodemographic characteristics were measured at baseline as time‐fixed variables. These included age , sex , race/ethnicity , and state of residence classified according to census regions . We also included an indicator for whether an individual was living in a household above or below the Federal poverty line (FPL). Data for annual household income were recorded in categories; we calculated the median for each category and divided this by the number of individuals in the household to estimate the income per household member. This was used to classify individuals as living in a household above or below the poverty line according to the 2020 Federal Poverty Guidelines. We also included a variable reflecting household structure. Respondents reported their relationships to other household members, as well as the ages of those members; we used this to classify individuals into five categories of household structure .margins and the xbrcspline commands in Stata were used to generate linear predictions of drinking days and to estimate differences in the number of drinking days on given survey dates compared to March 11, 2020, respectively, in the overall sample and stratified by each sociodemographic subgroup [We used mixed‐effects linear regression models with a random effect for participant to accommodate repeated measures. Analyses were conducted in three stages. First, we estimated a series of models to examine the association of each sociodemographic characteristic with the average number of drinking days across the entire survey period. Second, we estimated a single model with the splines for days since March 10, 2020, as covariates to examine the trajectory of drinking days over time among all US adults. Third, we estimated a series of models with interactions between the splines for days since March 10, 2020, and each of the identified sociodemographic characteristics to determine whether trajectories of drinking days over time differed between sociodemographic subgroups. Wald tests were used to determine if interactions were statistically significant. The subgroup . March 1To test the sensitivity of our findings to the exclusion of non‐drinkers, we re‐estimated our models in the complete sample of drinkers and non‐drinkers across the study period.P < 0.05 level. Analyses were conducted using Stata version 16 (StataCorp) and R . This analysis was not pre‐registered and results presented in this study should be considered exploratory.All analyses incorporated survey weights that account for probabilities of sample selection and survey non‐response and are aligned with Current Population Survey benchmarks. Missing observations because of survey non‐response were handled with full information maximum likelihood estimation. Statistical significance was assessed at the Across the study period, the overall average number of drinking days among participants who reported alcohol use was 2.23 days  in the past 7 days.Sample characteristics and differences in the number of drinking days across the study period are reported in Table Differences in the number of drinking days on selected dates, compared to March 11, 2020, are reported in Table Results for each sociodemographic subgroup are displayed in Table There were sociodemographic differences observed between those in the full sample compared to those included in the analytic sample with respect to sex, age, race, household structure, and poverty status  ranging from 9%–51%. Furthermore, it is important to note that number of drinking days in isolation may yield an incomplete picture of changes in alcohol consumption, because we did not have consistent information on quantity of alcohol consumption (e.g. number of drinks per day), which could provide more context to these observed changes.In previous research, certain sociodemographic characteristics have been associated with alcohol consumption , 30, 31.Although all age groups demonstrated increases in the number of drinking days, this increase was sustained most notably among those ages 65 and older, a particularly vulnerable group related to adverse effects from social isolation . Our finFinally, there was a sustained increase in drinking days observed for those living above the FPL, whereas those living below the FPL returned to levels comparable to baseline. This finding is consistent with other studies who have observed increased alcohol consumption among those in higher income brackets . ReductiWe recommend public health efforts, such as education, screening and surveillance, to support vulnerable subgroups and to avert both sustained alcohol consumption and potential transitions to problematic drinking. It is important to provide public health warnings about excessive alcohol consumption to prevent adverse effects of problematic alcohol use and to promote alternative positive coping strategies in response to stressful experiences. Although there are various motives for alcohol consumption, research has found that individuals who drink to cope in response to stress are at heightened risk for alcohol‐related problems , 38, 39.It is also important to consider the environment in which individuals engage in alcohol consumption. With the closure of bars and restrictions on social gatherings, it is possible that there could be increased solitary drinking, which has been linked to symptoms of alcohol use disorder and other adverse mental health outcomes , 46, 47.There are limitations of this study that are important to note. First, the survey did not collect data on the total number of standard drinks per drinking day. Therefore, we are unable to examine the prevalence of binge drinking and potential changes in the quantity of alcohol consumption. Second, survey dates were not randomly assigned at the first wave of data collection, and differences among participants who responded on earlier dates compared to those who responded on later dates could bias the observed results, though based on our sensitivity analysis, we do not have evidence to suggest that this caused significant bias in our analysis. Third, there were a number of sociodemographic characteristics that are known to be related to drinking behavior that were not examined such as sexual or gender identity, or time‐varying covariates like employment status. Future research should examine changes in and trajectories of drinking behavior in these groups. Fourth, there were some sociodemographic differences observed between participants who responded to all surveys compared to those who missed at least one survey. To the extent that participants who missed at least one survey collection period differed in their trajectory of drinking behavior, this may have biased our findings. Fifth, there were sociodemographic differences observed between those included in the analytic sample  compared to those who reported no alcohol use, which may have affected the representativeness of our sample. Finally, the study used measures of drinking behavior on March 11, 2020 as the baseline for comparison and it is possible that some changes in drinking behavior in response to the pandemic had already occurred before that date.Throughout the COVID‐19 pandemic, frequency of drinking among US adults has increased, peaking in early May, and remained at increased levels through mid‐July. Increased levels of drinking days were observed in some sociodemographic subgroups, particularly among men, White adults, those above the federal poverty line, and older adults. Supportive efforts and resources to prevent short‐ and long‐term problematic alcohol consumption during and after the COVID‐19 pandemic should be targeted at the population at large, as well as selectively at key subgroups that are identified to be at higher risk. As the pandemic continues, monitoring of alcohol consumption, as well as the incidence of problem drinking and alcohol use disorder, will be important priorities for public health surveillance and research.www.icmje.org/coi_disclosure.pdf and declare: J.T., F.K., and E.A.S. report grants from National Science Foundation during the conduct of the study; J.T. also reports grants from Johns Hopkins Alliance for a Healthier World, Capital Group during the conduct of the study; no other relationships or activities that could have appear to have influenced the submitted work. The manuscript's guarantor affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned  have been explained.All authors have completed the ICMJE uniform disclosure form at Courtney Nordeck: Conceptualization; writing ‐ original draft; writing ‐ review & editing. Kira Riehm: Conceptualization; formal analysis; writing ‐ original draft; writing ‐ review & editing. Emily Smail: Formal analysis. Calliope Holingue: Formal analysis. Jeremy Kane: Writing ‐ review & editing. Renee Johnson: Writing ‐ review & editing. Cindy Veldhuis: Writing ‐ review & editing. Luther Kalb: Writing ‐ review & editing. Elizabeth Stuart: Methodology; writing ‐ review & editing. Frauke Kreuter: Writing ‐ review & editing. Johannes Thrul: Conceptualization; methodology.Table S1 Number of observations for each survey date.Table S2 Pearson's χ2 comparisons between participants completing all 9 waves (n = 2673) and those completing 8 waves or fewer (n = 1625).Table S3 Comparison of drinkers and non‐drinkers on sociodemographic characteristics (n = 6605).Table S4 Differences in the number of drinking days on different dates in the survey period, compared to 03/11/2020, overall and stratified by sociodemographic characteristics, among US adult drinkers and non‐drinkers in the UAS Panel, 2020 (n = 6605).Figure S1 Flow diagram of response rates, proportion of observations from ever drinkers, and proportion of complete observations at each wave.Click here for additional data file.


The Global Leadership Initiative on Malnutrition (GLIM) proposed a new two-step model for diagnosing malnutrition in 2019. A combination of two etiologic and three phenotypic criteria are used to assess malnutrition. The Subjective Global Assessment (SGA) is the most well validated assessment tool for diagnosing malnutrition in hospitalized patients. Evaluation of the performance of GLIM criteria in comparison to SGA is necessary before implementing this new diagnostic tool in practice.To compare GLIM criteria to SGA in assessing malnutrition severity in hospitalized patients requiring parenteral nutrition (PN).This is a retrospective analysis of a prospectively collected database of malnourished hospitalized adult patients requiring PN admitted between March 2020 and March 2021 to an academic hospital in Winnipeg, Canada. 172 cases were evaluated. GLIM malnutrition screening was considered positive if one etiologic (high CRP or low food intake) and one phenotypic (weight loss or low BMI) criteria were identified. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated and expressed with a Wilson 95% confidence interval.The prevalence of malnutrition using SGA B or C criteria was 82.3% . Using GLIM criteria, the prevalence of malnutrition was 33.7% . The prevalence of severe malnutrition using SGA C was 32.6%  and using GLIM criteria the prevalence was 19.2% . Using any combination of GLIM criteria versus SGA B or C combined, the PPV was 100%  and the specificity was 100% ; NPV was 30.0%  and the sensitivity was 42.0% . Using any combination of GLIM criteria versus only SGA C patients, PPV decreased to 72.4%  and specificity was 86.2% ; NPV increased to 87.7%  and sensitivity was 75.0% . Comparing severely malnourished patients by GLIM criteria to only SGA C patients, PPV was 97%  and specificity was 99.1% . NPV was 82.7%  and sensitivity was 57.1% .Using SGA as the gold standard for diagnosing malnutrition in hospitalized patients requiring PN, GLIM criteria had a very high PPV but unacceptably low NPV in diagnosing malnutrition. The NPV improved when GLIM criteria was compared only to severely malnourished patients by SGA. Importantly, in comparing severely malnourished patients by GLIM and SGA criteria, the sensitivity was also unacceptably low. Based on these results, GLIM criteria are most useful in confirming the diagnosis of malnutrition or severe malnutrition; a negative result should not reassure clinicians that severe malnutrition is absent. Further studies evaluating GLIM criteria are needed before it replaces SGA as a decision making tool.None


Due to the COVID-19 pandemic, major parts of elective health care in the Netherlands, such as reproductive medicine, were paused. When health care was resumed, video consultation was used as a new solution to continue consultations with the new governmental rules of social distancing. Prior to this COVID-19 situation, video consultation was not used extensively in the Netherlands; therefore, physicians and patients are not familiar with this way of consultation.The purpose of this study was to measure the level of patient centeredness and shared decision making in infertile couples who have undergone fertility workup through video consultation.This is a questionnaire study with an additional qualitative part for a more in depth understanding. Infertile couples  were referred to a fertility center and underwent fertility workup through video consultation. The fertility workup consisted of 2 separate video consultations, with diagnostic tests according to a protocol. After the last video consultation couples received a digital questionnaire, which consisted of a modified version of the Patient-Centered Questionnaire-Infertility (PCQ-I) and CollaboRATE questionnaire. Fifty-three eligible infertile couples were approached, and of these, 22 participated. Four women were approached for a semistructured interview.The median score on the modified PCQ-I  was 2.64. The highest rating was for the subscale communication and information, and the lowest rating was for the subscale organization of care. The median score on the CollaboRATE questionnaire  was 8 for all 3 subquestions. Patients mentioned privacy, less travel time, and easy use of the program as possible benefits of video consultation. However, patients preferred the first consultation with their physician to be face-to-face consultation as video consultation was considered less personal.The high levels of patient centeredness and shared decision making show that video consultation is a promising way of providing care remotely, although attention has to be payed to mitigate the more impersonal setting of video consultation when compared with face-to-face consultation. In the initial months of 2020, the COVID-19 pandemic put a stop to most societal activities and major parts of elective health care, such as reproductive medicine. If possible, on-going in vitro fertilization cycles were completed, after which fertility treatments were postponed, based on the advice of both the Dutch and European scientific societies. When elective health care was restarted after a few months, medical departments faced new challenges, having to implement governmental rules on social distancing and minimizing patient traffic in hospitals. One of the solutions for these challenges, in order to continue consultation without physical visits to the hospital, is video consultation.Video consultation has existed for some time and was mentioned before as a solution to scarcity in health care. Although it was not being used extensively in the Netherlands, it seems to have advantages for patients, such as less travel time and parking costs, and avoidance of waiting in the hospital. Besides, patients are not dependent on others for hospital visits and can experience privacy in the comfort of their own homes. However, technical issues, inferior computer skills, data security issues, and a less personal approach compared with face-to-face consultation are possible pitfalls ,2. The lFertility Consult. It involves a secured online platform [Fertility Consult have recently been published [We have been applying video consulting for first consultations for several years in our project platform  for infeublished .The decision to pause elective health care during the COVID-19 pandemic had a big impact on all patients, with infertility patients being no exception to the rule. It is known that involuntary childlessness is a heavy burden on patients’ quality of life. Therefore, infertile patients benefited from quick resumption of care, with a form of care that fitted with the social distancing rules . To assess how this care was experienced, a pilot study was set up at the Reproductive Medicine Centre in Jeroen Bosch Hospital, The Netherlands. The aim of this pilot study was to explore patient experiences with a fertility workup through video consultation and extract the possible advantages and disadvantages of video consultation. These results can be used to improve the implementation and quality of video consultation in daily fertility care.We performed a study consisting of both quantitative and qualitative parts to evaluate the experiences of infertile couples with an online fertility workup and obtain more in-depth understanding of their experiences.For the online fertility workup, couples with an unfulfilled wish for a child were referred by their general practitioner after having unprotected intercourse for more than 12 months, just as before the COVID-19 pandemic. Couples completed a questionnaire about their medical situation, after which a video consultation was scheduled instead of a regular appointment in the outpatient clinic. This video consultation was carried out by either a gynecologist or a fertility physician. The program used for the video consultation was called Webcam Consult , which wConsequently, a second video consultation was performed, with the same physician as in the first video consultation, to discuss the results, diagnosis, and possible therapy. After the second video consultation, all couples were asked to participate in the study, which was performed between May and June 2020. Those who did not understand the Dutch language were excluded, as the questionnaire was only available in Dutch. One questionnaire was sent per couple, which could be completed in a secured online environment. Nonresponders received 2 reminders. Four patients were approached, at random, to participate in a semistructured interview. A separate written consent form was collected before the interview.As the study had minimal impact on the study subjects , it was judged that the Dutch Medical Research with Human Subjects Law was not applicable. Therefore, approval of the medical ethics committee was not needed.The quantitative part consisted of a modified version of the validated Patient-Centered Questionnaire-Infertility (PCQ-I)  and the The original PCQ-I consists of 7 subscales with 46 questions covering many different subjects of patient-centered infertility care, such as information provision, communication, and continuity of care. The PCQ-I scale has a range from 0 to 3, where a higher score implies a higher level of patient centeredness. Since not all questions were suitable for the specific video consultation setting, we used the modified PCQ-I, as used in the study by Huppelschoten et al , which cThe CollaboRATE questionnaire consists of the following 3 questions: How much effort was made to help you understand your health issue? How much effort was made to listen to the things that matter most to you about your health issues? and How much effort was made to include what matters most to you in choosing what to do next? Patients answer these questions on a 10-point Likert scale from 0 (“no effort was made”) to 9 (“every effort was made”).In addition to the 2 questionnaires, some questions were added about the technical and practical aspects of video consultation.For the qualitative part, several women were approached for semistructured interviews to investigate in more depth the possible advantages and disadvantages of participating in video consultation. A topic list was drafted in advance, based on available literature, and discussed with a small team of experts.The interviews were held by telephone. The conversation was recorded and transcribed to extract patient comments. Interviews were held until saturation of information was reached. The participants of the interviews also completed the PCQ-I and CollaboRATE questionnaire.The data from the questionnaires were collected from a secure online environment called “Digitale kinderwens poli” , which is used in a clinical setting, and exported to Excel. The data from the questionnaire and from the semistructured interviews were handled by a local investigator, who had access to the participant code. For the other members of the research group, the data were anonymous. The data were stored according to the standards of the local facility.The demographics and background characteristics of the participants and the results of the PCQ-I and CollaboRATE questionnaire were analyzed using descriptive statistics. The normally distributed outcome measures are represented by means of the means with standard deviations, and the nonnormally distributed outcomes are represented by means of the medians. Analyses were performed using SPSS version 22 (IBM Corp).Out of the 53 couples who completed the online fertility workup, 22 responded (response rate 42%). The mean age was 31.7 years (SD 4.1 years). The characteristics of all patients are summarized in The results of the PCQ-I and CollaboRATE questionnaire are presented in Four interviews were held with female patients. The interviews were analyzed, and quotations were categorized. Acceptance of video consultation was high, given the current COVID-19 situation. Three women expressed that for the first consultation, in a more optimal situation without the COVID-19 pandemic, they would prefer to visit their physician in the hospital.Example quotations can be found in Information- A video consultation is a good replacement for a conversation, a telephonic call is inferior.- It worked perfect to hear the results of the tests per video consultation.Privacy- I was not worried about the privacy.- I felt very secure in my own environment.Communication/respect for patient value- I would prefer to talk to my physician face-to-face on the first appointment, because it is possible that video consultation lacks body language.- I found the video consultation a bit impersonal.Involvement of partner- I could evaluate the appointment with my partner immediately, because we were already home.Organization of care- I don’t like waiting in waiting rooms, and now I was able to wait at home.- We saved a lot of time, without traveling to the hospital.Technical aspect- Everything went very smooth, no problems.- There was a clear instruction beforehand.- On beforehand I was a bit worried if the connection would be good enough.- We didn’t have any sound, so the doctor called us. That made it a bit chaotic.This is one of the first studies to explore patient experiences with video consultation in the context of the first consultation. In general, patients perceived a high level of patient centeredness as reflected in a high score on the PCQ-I. Video consultation also scored high in shared decision making on the CollaboRATE questionnaire. During the interviews, patients expressed a relatively high satisfaction rate with video consultation, although they did express that their preference would be to meet their physician face-to-face for the first consultation.An important strength of this study is that, to our knowledge, this is one of the first studies to explore patient experiences in their first contact with a new medical department. As both quantitative and qualitative techniques were used, we were able to gather more in-depth information about patient experiences. Besides, we used 2 validated questionnaires, which can be used in the future to compare results.Some limitations should be mentioned as well. First, since this was a pilot study, the sample size was rather small. The response rate was only 42%, and it would be interesting to know why the other patients did not respond. Second, the relatively high median scores on the PCQ-I and CollaboRATE questionnaire may be affected by the circumstances of the current COVID-19 pandemic [In general, literature on video consultation in health care is limited. Often, only small groups were studied in different settings and almost exclusively using video consultation in the context of a follow-up consultation. Previous Dutch research did study the preferences of a larger group (968 patients), but only 1.7% of the responders had prior experience with video consultation . In anotA British study in primary care showed that video consultation involved less information when compared with face-to-face contact. For example, during video consultation, patients raised fewer issues, the physician provided less information, and the psychosocial context was more often lost . Our stuIn addition to the content of the video consultation, its implementation is important. In this pilot study, technical problems were recorded in 40% of cases, which is a relatively high percentage. A different British study emphasized this. In the study, clinicians and patients were interviewed after a video consult, and both groups mentioned that technological problems sometimes disrupted the consultation . Both thThis pilot study explored infertile patients’ experiences using video consultation for their first contact with a gynecologist, after being referred for an unfulfilled wish of having a child. This study showed high scores for patient centeredness and shared decision making. Video consultation is a way of providing care remotely and could potentially be superior to other forms of telecommunication, such as telephone and email contact. Although the acceptance of video consultation might be influenced by COVID-19 circumstances, we do think that the results are promising for the future.As a next step, we will continue research on video consultation in fertility care. A randomized controlled trial will compare online fertility workup with the standard fertility workup involving face-to-face consults  in terms of patient centeredness and health care costs. Future studies should also focus on how to promote the implementation of video consultation in our current health care setting and minimize technical issues, so that video consultation will be continued after the COVID-19 pandemic.