The model takes into account the full 2 × 3 data structure of lineup procedures, that is, suspect identifications, filler identifications and rejections in both culprit-present and culprit-absent lineups. Here the model is introduced and the results of four large validation experiments are reported, one for each of the processes specified by the model. The validation experiments served to test whether the model’s parameters sensitively reflect manipulations of the processes they were designed to measure. The results show that manipulations of exposure duration of the culprit’s face at encoding, lineup fairness, pre-lineup instructions and ease of rejection of culprit-absent lineups were sensitively reflected in the parameters representing culprit-presence detection, biased suspect selection, guessing-based selection and culprit-absence detection, respectively. The results of the experiments thus validate the interpretations of the parameters of the 2-HT eyewitness identification model.To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The  The suspect’s face is to be presented among the faces of known innocent fillers. The witness’s task is to identify the culprit or reject the lineup. However, instead of correctly identifying the culprit or rejecting the lineup with an innocent suspect, the witness may falsely reject a lineup even though the culprit is present or select a known innocent filler. In the worst case, the witness identifies an innocent suspect2.Eyewitness identifications often are essential for convicting culprits. Given that eyewitness identification decisions are made after the crime has occurred, it is necessary to assess the witness’s memory to test the hypothesis that a suspect is the culprit. The memory test is usually done in the form of a lineup. Lineup procedures have to follow strict protocolstwo-high threshold (2-HT) eyewitness identification model that measures four different latent processes underlying eyewitness identification performance: detection of the presence or absence of the culprit, biased suspect selection and guessing-based selection. To validate this model, we tested in four large experiments whether the four model parameters respond sensitively to manipulations that can be expected to influence the postulated latent processes represented by the parameters.To improve police protocols for lineup procedures, it is helpful to understand the cognitive processes upon which these eyewitness identification decisions are based. Here we introduce the 3. At first glance, it may seem desirable that lineup procedures lead to a high rate of correct culprit identifications. However, this is so only if all culprit identifications are based on the detection of the culprit. Unfortunately, however, correct culprit identifications may also be caused by the biased selection of the suspect due to an unfair lineup procedure in which the suspect stands out from the fillers or by a tendency to select one of the lineup members based on guessing. Both of these processes do not only lead to correct culprit identifications but also to false innocent-suspect identifications. To understand eyewitness identification performance, it is thus important to distinguish between different latent processes that may contribute to eyewitness identification decisions.Many studies have examined how police lineup procedures affect the quality of eyewitness identification decisionsdiagnosticity ratio9 which is defined as the ratio between the proportion of correct culprit identifications and the proportion of false innocent-suspect identifications10. A higher diagnosticity ratio was taken to indicate superior eyewitness identification performance. However, the diagnosticity ratio attains higher values not only when a procedure is associated with better discrimination between culprits and innocent suspects but also when witnesses exhibit a conservative response bias13. Therefore, it has been suggested to apply Receiver Operating Characteristic (ROC) curves to the analysis of eyewitness identification decisions18. Derived from signal detection theory (SDT)19, ROC curves are constructed by plotting the hit rate  against the false alarm rate  at different levels of response bias. In lineup research, the hit rate corresponds to the rate of correct culprit identifications. The false alarm rate corresponds to the rate of false innocent-suspect identifications. The levels of response bias are usually derived from confidence judgements that witnesses are asked to assign to their identification decisions. To measure the degree to which the procedure allows witnesses to discriminate between culprits and innocent suspects, the partial area under the curve (pAUC) is calculated. The lineup procedure with the greater pAUC is to be preferred20.To measure eyewitness identification performance, researchers have often relied on the 19 that was originally proposed for detection problems with a 2 × 2 data structure  models that has proven useful to analyze, and to test hypotheses about, the latent processes underlying observable behavior in various fields of psychology, including memory (e.g.24) and decision making (e.g.27). There are several excellent introductions to MPT modeling29. Parameter estimation and statistical tests on the model parameters can be performed with freely available computer programs32.It has been argued that, for the purpose of deciding which of two lineup procedures is superior, it is sufficient to focus only on correct culprit identifications and false innocent-suspect identifications because these data categories have the most far-reaching consequences in practicedP (for detection of the presence of the culprit). If culprit detection fails (with probability 1 − dP), the witness may identify the culprit based on two types of non-detection-based processes. If the lineup is unfair, the culprit stands out from the fillers so that it can be inferred who the suspect is without relying on memory. In such cases, biased selection of the suspect occurs with probability b (for biased suspect selection). With probability 1 − b, no biased suspect selection occurs . In this case, it is still possible to select one of the lineup members as the culprit based on guessing. Guessing-based selection differs from biased suspect selection in that, when the witness selects one of the lineup members based on guessing, the culprit is selected among the fillers with a probability that is equal to 1 ÷ lineup size. With probability 1 − (1 ÷ lineup size), one of the fillers is selected. To illustrate, in a six-person lineup, the probability that selecting one of the lineup members based on guessing results in the identification of the suspect among the fillers is 1/6 while the probability of selecting one of the fillers is 5/6. The probability with which guessing leads to a suspect identification is a function of the size of the lineup and does not depend on the witnesses’ internal cognitive representation which is why this element of the model is a constant and not a parameter that has to be estimated from data. If none of the lineup members is selected based on guessing (with probability 1 − g), the lineup is falsely rejected.A graphical illustration of the 2-HT eyewitness identification model is displayed in Fig. dA (for detection of the absence of the culprit), leading to the correct rejection of the lineup. With probability 1 − dA, the absence of the culprit is not detected, in which case the same non-detection-based processes are postulated as in culprit-present lineups. An obvious characteristic of guessing-based selection (parameter g) is that it must not differ between culprit-absent and culprit-present lineups. The same applies to biased suspect selection (parameter b): the police do not know whether their suspect is the culprit or innocent. Therefore, if a lineup is unfair, it is equivalently unfair for culprits and innocent suspects. For reasons of ecological validity, it is thus optimal to have a designated innocent suspect who deviates from the fillers in a way that is similar to how the culprit deviates from the fillers when studying the effects of lineup unfairness.For culprit-absent lineups in which the suspect is innocent , they can be estimated separately in the 2-HT eyewitness identification model (cf.35). Given that the model does not have to be restricted by setting the dA parameter equal to another parameter, it is possible to determine at an empirical level whether eyewitnesses are able to detect the absence of the culprit. To anticipate, the results of the present Experiment 4 and reanalyses of published studies36 suggest that people detect the absence of the culprit in some situations but fail to detect the absence of the culprit in others.The 2-HT eyewitness identification model is a two-high threshold model such as, for example, the widely used two-high threshold model of source memory28. A successful validation requires that the model fits the empirical data and that the model parameters respond sensitively to manipulations of the processes they were designed to measure. For this purpose, validation experiments are needed in which well-established or outright trivial manipulations of the latent cognitive processes are used to test whether the changes in the latent cognitive processes are reflected in the model’s parameters. Here, we report four validation experiments, one for each of the four parameters of the model. To influence the detection of the presence of the culprit, reflected in parameter dP, we manipulated the exposure duration of the culprits’ faces at encoding. To influence biased suspect selection, reflected in parameter b, we manipulated the unfairness of the lineup. To influence guessing-based selection, reflected in parameter g, we manipulated whether the pre-lineup instructions suggested either a high or a low probability of the culprit being in the lineup. To influence the detection of the absence of the culprit, reflected in parameter dA, we compared the standard lineup procedure with a condition in which all lineup members in culprit-absent lineups could easily be ruled out as the culprit. Given the lively discussion about whether lineups should be simultaneous or sequential39, one group of participants saw simultaneous lineups while another group of participants saw sequential lineups in each of the four experiments.In a new MPT model it is necessary to validate the interpretation of the model parametersdP which is assumed to represent the probability of detecting the presence of the culprit. A culprit’s presence in a lineup is easier to detect when the culprit’s face was exposed for a long than for a short duration during a crime42. We thus presented long or short crime videos. If the 2-HT eyewitness identification model is valid, parameter dP should be higher in the long-exposure condition than in the short-exposure condition.Experiment 1 served to test the validity of parameter Gapfish research panel (https://gapfish.com). Participants had to be of legal age (≥ 18 years) and had to have good eyesight and German language skills. A power analysis with multiTree30 showed that, given α = β = 0.05, at least N = 628 participants were needed to detect a difference in the dP parameter between the long- and short-exposure condition of ΔdP = 0.12 (corresponding to an effect size of w = 0.07), estimated from a pilot laboratory study. A somewhat larger sample was recruited to compensate for the loss of data that can be expected in online studies. In total, 932 participants gave informed consent, but 135 of them did not complete the study. A total of 33 data sets had to be excluded because of multiple participations . Another 22 data sets had to be excluded because participants had failed the attention check (see below) or because they had reported technical problems. The final sample included 742 participants (327 women and 415 men) with a mean age of 44 years (SD = 15).Participants were recruited using the The following applies to all experiments reported here. Prior to participation, informed consent was obtained from all participants. Participants were warned that they would see a video containing verbal and physical abuse. They were asked not to continue if they felt uncomfortable when anticipating to view such a video. After the experiment, participants were informed about the purpose of the study and assured that the crime was staged. Ethical approval had been received from the ethics committee of the Faculty of Mathematics and Natural Sciences of Heinrich Heine University Düsseldorf. The study was run in accordance with the declaration of Helsinki. Informed consent for the publication was obtained from the actors of the staged-crime videos to show the stimulus material.SoSci Survey43. Participants were only allowed to participate with computers or laptops, not with smartphones or tablets. Participants were asked to complete the study alone in a quiet environment. Prior to the experiment proper, participants were asked to bring their browsers into full-screen mode.The experiment was conducted online using 48). This procedure generates only a single data point per participant. In order to increase the efficiency of data collection, researchers have introduced between two and at least 14 to-be-identified persons in their lineup studies58. We followed this lead and presented our participants with a crime video containing four culprits. Specifically, the crime video showed four alleged hooligans of the German soccer club FC Bayern München attacking an alleged fan of a rivaling soccer club, Borussia Dortmund, at a bus station  lineup at test , the important exception being that the victims and the culprits were different persons. However, care was taken that the victim in Video 1 was as similar as possible to the victim in Video 2 and that each of the four culprits in Video 1 was as similar as possible to one of the four culprits in Video 2 . It was randomly determined which of the two parallel versions of the video was shown. The videos were shown in a resolution of 885 × 500 pixels.For each of the two parallel videos, we created a long version that lasted about 130 s and a short version that lasted only 13 s (the rest of the video was cut off). In both versions of the videos, the culprits’ faces were clearly visible from a frontal view. Participants were randomly assigned to one of the two exposure-duration conditions. About half of the participants saw the long video while the other half of the participants saw the short video.Participants could start the video by clicking on a “Start” button. The control elements of the video player were disabled so that participants could not fast-forward, replay or stop the video. After the video had been presented, participants received an attention-check question about the persons they saw in the video. They passed the attention check when they were able to remember that “soccer fans” had been shown in the video by choosing this option among ten alternatives. The option “athletes” (intended to be one of the nine distractors) was frequently chosen instead of the nominally correct option which suggests that this option was unintentionally ambiguous. Therefore, selecting this option was counted as correct in Experiment 1. This option was replaced by the less ambiguous option “waiters” in all subsequent experiments.Participants were informed that they had to identify the FC Bayern München hooligans from the video they had just seen. Participants received two-sided lineup instructions that emphasized both the need to identify the culprit if the culprit was present and the need to reject the lineup if the culprit was absent. Participants were not informed about how many lineups were about to follow.59. Given that all suspects were young male adults, 20 pictures of male adults aged between 18 and 29 years were chosen as fillers. The fillers roughly resembled the culprit or innocent suspect in hair color, hairstyle and stature. Together with the fact that it was randomly determined whether participants saw Video 1 or Video 2, this procedure served to ensure that the culprits and innocent suspects differed to the same degree, on average, from the fillers in the lineup. In this way the procedure is highly ecologically valid because the situation corresponds closely to that of a real-world lineup in which the photograph of the suspect (whose status as culprit or innocent suspect is unknown to the police), stems from a different source  than the photographs of the fillers  and may thus differ to some degree from that of the fillers. All photographs showed the faces from a frontal view with a neutral facial expression against a black background with no clothes visible. The photographs were edited to harmonize face sizes and lighting conditions and were presented in a resolution of 142 × 214 pixels. The order of the lineups was randomized, as was the position of the suspect and the fillers in each lineup.Participants saw four separate lineups, two culprit-present lineups and two culprit-absent lineups, in random order. Each of the four lineups consisted of the facial photographs of six persons, one suspect and five fillers. In each of the two culprit-present lineups, a randomly selected face of one of the hooligans of the video the participants had seen was presented among the fillers. In each of the two culprit-absent lineups, the face of an innocent suspect was presented that the participants had not witnessed committing a crime. The innocent suspect was one of the hooligans from the video that the participants had not seen. The innocent suspect had the same hair color, hair style and stature as one of the hooligans from the video the participants had seen. For instance, if participants had seen Video 1, two randomly selected hooligans  from Video 1 served as the culprits in the two culprit-present lineups, while two of the hooligans from Video 2 (Hooligan A and Hooligan D in this example) served as the innocent suspects in the culprit-absent lineups. The pictures of the fillers were taken from the CVL database of Minear and Park60 and other jurisdictions (cf.63) and parallel to the simultaneous lineups in which participants were able to revise their decision before they clicked on the “Continue” button. To make the procedure similar to that of a typical police lineup, participants also indicated how confident they were that their decision was correct. The lineup was rejected if none of the members of the lineup was identified as one of the culprits.It was randomly determined whether participants saw simultaneous or sequential lineups Fig. . In a simultiTree30. One instance of the model illustrated in Fig. For all analyses reported in this article, model fits and parameter estimates were obtained using b to be equal across all conditions. Second, for the same reason parameter dA was also set to be equal across all conditions. The base model incorporating these trivial restrictions fit the data, G2(6) = 3.91, p = 0.689. Multinomial processing-tree models allow to test hypotheses directly at the level of the postulated processes. For instance, the hypothesis that the culprit-presence detection parameter dP is higher in the long-exposure condition than in the short-exposure condition can be implemented by setting parameter dP to be equal between these conditions. If the model including this equality restriction provides a significantly worse fit to the data than the base model, then it is necessary to conclude that parameter dP differs between conditions. The estimates of the culprit-presence detection parameter dP are shown in Fig. dP was significantly higher in the long-exposure condition than in the short-exposure condition, ΔG2(2) = 72.21, p < 0.001.Our goal was to begin with a base model that was as simple as possible. Therefore, we used whatever we could derive from the design of the studies to impose restrictions onto the 2-HT eyewitness identification model. First, it is obvious that lineup fairness must necessarily be the same across conditions because the same lineups were used in all conditions. Therefore, we set the biased-suspect-selection parameter b, g and dA are reported in Table g) was significantly higher when exposure duration was short than when it was long, ΔG2(1) = 6.47, p = 0.011, which is to be expected given the well-known phenomenon of compensatory guessing67. Interestingly, exposure duration had no effect on guessing-based selection in the sequential lineups, ΔG2(1) = 0.03, p = 0.871, suggesting that the sequential presentation of the faces in the lineup prevented compensatory guessing.The estimates of parameters dP was significantly higher in the long-exposure condition than in the short-exposure condition. The validation of parameter dP was thus successful; parameter dP sensitively reflected the manipulation of culprit-presence detection in the predicted direction.Long exposure of the culprit’s face provides ample opportunity for encoding which should increase the probability of detecting the culprit’s face in a lineup. Experiment 1 confirmed this prediction. Parameter g was also affected by exposure duration. In the short-exposure condition in which culprit-presence detection was poor, guessing-based selection was more prevalent than in the long-exposure condition in which culprit-presence detection was better. This effect on parameter g is expected given the well-known phenomenon of compensatory guessing: people often rely on guessing to compensate for poor memory67. Note that the simplest situation in model validation is one in which only the target parameter (dP in the present experiment) is affected by the manipulation. This is not always possible because manipulations may have side effects. The ideal situation in such cases is one in which there is an obvious and plausible explanation of these side effects—such as compensatory guessing in the present instance. Interestingly, evidence of compensatory guessing was absent in the sequential-lineup condition. The sequential presentation of the faces may thus protect against compensatory guessing: in sequential lineups, participants may not increase their reliance on guessing when culprit-presence detection is poor because they cannot know whether the face of the culprit is yet to be presented. In simultaneous lineups, it is more obvious that the culprit cannot be detected, which may provide more favorable conditions for compensatory guessing. This hypothesis can be further explored in future research.In simultaneous lineups, parameter b which is assumed to represent the probability of biased selection of a culprit or an innocent suspect who stands out from the fillers. Biased suspect selection is thus assumed to occur when lineups are unfair. In Experiment 1, care was taken to create fair lineups in which the suspect did not stand out from the fillers. The fact that the estimate of parameter b was extremely low suggests that biased suspect selection occurred with a very low probability. In Experiment 2, this fair-lineup condition was contrasted with an unfair-lineup condition. A straightforward method of creating unfair lineups is to add a conspicuous feature such as a birthmark to the suspect’s face that distinguishes this face from the filler faces68. In line with this approach, unfair lineups were created by adding large birthmarks to all filler faces but not to the faces of the culprits and innocent suspects. The suspect’s face thus stood out from these fillers in that it was the only face without a birthmark. If the 2-HT eyewitness identification model is valid, then the biased-suspect-selection parameter b should be higher in the unfair-lineup condition than in the fair-lineup condition.Experiment 2 served to test the validity of parameter Gapfish research panel. We aimed at achieving about the same sample size as in Experiment 1. In total, 930 participants gave informed consent but 139 of them did not complete the study. A total of 19 datasets had to be excluded because of multiple participations. Another 16 datasets had to be excluded because participants had failed the attention check or because they had reported technical problems. The final sample included 756 participants (355 women and 401 men) with a mean age of 44 years (SD = 15). A sensitivity analysis with G*Power69 showed that with a sample size of N = 756, four eyewitness identification decisions, α = 0.05 and a statistical power of 1 − β = 0.95, it was possible to detect even small effects of the unfairness on the biased-selection parameter b of size w = 0.07.Participants who had not participated in Experiment 1 were recruited using the Experiment 2 was identical to Experiment 1, with the following exceptions. Participants saw the full-length staged-crime video used in the long-exposure condition of Experiment 1. Unfair lineups were created by digitally manipulating the filler photographs to create lineups in which all lineup members except the suspect had large birthmarks on their faces Fig. . In the dA was set to be equal across all conditions. The base model incorporating these restrictions fit the data, G2(3) = 2.32, p = 0.509. The estimates of the biased-suspect-selection parameter b are shown in Fig. b was significantly higher when the lineup was unfair than when it was fair, ΔG2(2) = 28.59, p < 0.001.One instance of the model illustrated in Fig. dP, g and dA are reported in Table dP) did not differ as a function of lineup fairness, ΔG2(2) = 0.12, p = 0.940. Guessing-based selection (captured by parameter g) was less likely when the lineup was unfair than when it was fair, but this difference was significant only in the simultaneous lineup condition, ΔG2(1) = 7.93, p = 0.005, and not in the sequential lineup condition, ΔG2(1) = 0.09, p = 0.760.The estimates of parameters b represents the process of biased suspect selection: parameter b was higher in the unfair conditions than in the fair conditions. In simultaneous lineups, lineup fairness also affected parameter g. A possible explanation is that guessing-based selection may have been reduced by the relative ease with which the fillers could be ruled out due to their large birthmarks in the unfair-lineup condition.The results support the validity of the interpretation that parameter g which is assumed to reflect the probability of guessing-based selection among the lineup members. A well-established way to manipulate guessing in old-new recognition paradigms is to create the expectation  that a stimulus is more likely to be old than new or vice versa70. Similarly, one-sided pre-lineup instructions insinuating that the culprit is in the lineup increase the witnesses’ willingness to guess that one of the lineup members is the culprit compared to two-sided pre-lineup instructions that emphasize the possibility that the culprit may not be in the lineup (e.g.72). For instance, Lindsay et al.73 manipulated guessing by providing either one-sided pre-lineup instructions stating that “The guilty party is in the lineup, all you have to do is pick him out” or two-sided pre-lineup instructions stating that “the guilty party may or may not be in the lineup” (p. 799). Similarly, half of the participants in Experiment 3 received the instruction that the probability of the culprit being in the lineup was high which should encourage participants to select one of the lineup members as the culprit based on guessing. The other half of the participants received the instruction that the probability of the culprit being in the lineup was low which should discourage participants from selecting one of the lineup members as the culprit based on guessing. If parameter g of the 2-HT eyewitness identification model is valid, then parameter g should be higher in the high-culprit-probability condition than in the low-culprit-probability condition.Experiment 3 served to test the validity of parameter Gapfish research panel. We aimed at achieving about the same sample size as in Experiments 1 and 2. In total, 896 participants gave informed consent but 108 of them did not complete the study. A total of 23 datasets had to be excluded because of multiple participations. Another 11 datasets had to be excluded because participants had failed the attention check or because they had reported technical problems. The final sample included 754 participants  with a mean age of 45 years (SD = 15). A sensitivity analysis with G*Power69 showed that with a sample size of N = 754, four eyewitness identification decisions, α = 0.05 and a statistical power of 1 − β = 0.95, it was possible to detect even small effects of the pre-lineup instructions on the guessing-based-selection parameter g of size w = 0.07.Participants who had not participated in Experiments 1 and 2 were recruited using the “It is likely that there is one of the culprits in each of the lineups. Therefore, you should select the ‘Yes, was present’ button that belongs to the recognized face if one of the persons feels familiar”. Before each lineup, participants in the high-culprit-probability condition received the following reminder: “It is likely that one of the culprits is in the lineup. You simply have to choose him”. In the low-culprit-probability condition, the instructions read: “It is unlikely that one of the culprits is in the lineup. Therefore, you should select the ‘Yes, was present’ button that belongs to the recognized face only if you are very certain that you have recognized the right person”. Before each lineup, participants in the low-culprit-probability condition received the following reminder: “It is unlikely that one of the culprits is in the lineup. Please choose someone only if you are very certain”.Experiment 3 was identical to Experiment 1 with the following exceptions. Participants saw the full-length staged-crime video used in the long-exposure condition of Experiment 1. In the high-culprit-probability condition, the instructions read: b und dA were set to be equal across all conditions. The base model incorporating these restrictions fit the data, G2(6) = 3.85, p = 0.698. The estimates of the guessing-based selection parameter g are shown in Fig. g was significantly higher in the high-culprit-probability condition than in the low-culprit-probability condition, ΔG2(2) = 137.36, p < 0.001.One instance of the model illustrated in Fig. dP, b and dA are reported in Table dP) did not differ as a function of the pre-lineup instructions, ΔG2(2) = 4.13, p = 0.127.The estimates of parameters g represents the probability of guessing-based selection among the lineup members. Parameter g was higher when participants expected culprits in the lineups with a high rather than low probability.The results support the validity of the interpretation that parameter dA which is assumed to represent the probability of detecting the absence of the culprit. To manipulate parameter dA, the standard lineups were compared against lineups in which it could easily be detected that none of the lineup members was the culprit. In these easy-to-reject culprit-absent lineups, all lineup members, including the innocent suspect, shared a conspicuous facial feature that could be used to rule out the lineup members as culprits. These easy-to-reject lineups were compared to standard culprit-absent lineups that were difficult to reject because all lineup members, including the innocent suspect, matched the culprit in appearance. If the 2-HT eyewitness identification model is valid, the culprit-absence detection parameter dA should be higher when the lineup is easy to reject than when it is difficult to reject.Experiment 4 served to test the validity of parameter Gapfish research panel. We aimed at achieving about the same sample size as in Experiments 1 to 3. In total, 929 participants gave informed consent but 117 of them did not complete the study. A total of 22 datasets had to be excluded because of multiple participations. Another 18 datasets had to be excluded because participants had failed the attention check or because they had reported technical problems. The final sample included 772 participants  with a mean age of 46 years (SD = 15). A sensitivity analysis with G*Power69 showed that with a sample size of N = 772, four eyewitness identification decisions, α = 0.05 and a statistical power of 1 − β = 0.95, it was possible to detect even small effects of the ease of lineup rejection on the detection of the absence of the culprit parameter dA of size w = 0.07.Participants who had not participated in Experiments 1 to 3 were recruited using the Experiment 4 was identical to Experiment 1 with the following exceptions. Participants saw the full-length staged-crime video used in the long-exposure condition of Experiment 1. For the easy-to-reject condition, culprit-absent lineups were created by digitally manipulating the facial photographs such that all lineup members, including the innocent suspect, had large birthmarks on their faces, just like the filler faces in the unfair-lineup condition of Experiment 2 Fig. . This mab was set to be equal across all conditions. The base model incorporating these restrictions fit the data, G2(3) = 5.00, p = 0.172. The estimates of the culprit-absence detection parameter dA are shown in Fig. dA was significantly higher when the culprit-absent lineup was easy to reject than when it was difficult to reject, ΔG2(2) = 11.95, p = 0.003.One instance of the model illustrated in Fig. dP, b and g are reported in Table dP), ΔG2(2) = 2.93, p = 0.231, nor guessing-based selection (captured by parameter g), ΔG2(2) = 0.38, p = 0.827.The estimates of parameters dA represents the probability of detecting the absence of the culprit. Parameter dA was enhanced when the culprit-absent lineups were easy to reject because all lineup members, including the innocent suspect, had the same conspicuous facial feature which could be used to rule them out as culprits.The results support the validity of the interpretation that parameter dP sensitively reflected the manipulation of exposure duration that can be assumed to affect the detection of the presence of the culprit; parameter dP was larger when the culprits’ faces were visible for a long time than when they were visible for only a short time. In Experiment 2, the manipulation of lineup fairness was sensitively reflected in parameter b that was designed to capture the biased selection of the suspect which occurs in unfair lineups; parameter b was larger when a conspicuous facial feature distinguished the filler faces from the suspects’ faces than when this conspicuous facial feature was absent in all faces of the lineups. In Experiment 3, the manipulation of the pre-lineup instructions was sensitively reflected in parameter g that was designed to measure guessing-based selection; parameter g was larger when the participants were led to expect culprits in the lineups with a high rather than low probability. The effects demonstrated in Experiments 1 to 3 are closely aligned with well-established findings in the literature81. This reflects the deliberate strategy to use, for the purpose of validation experiments, manipulations that are well understood so that one can be certain as to which processes and, hence, which model parameters they should affect. This strategy was not readily available for the process of the detection of culprit absence, reflected in parameter dA, possibly because other approaches for analyzing lineup data do not specifically postulate a separate process for culprit-absence detection so that this process is less well studied. Therefore, an intentionally trivial manipulation was used in Experiment 4: culprit-absence detection should be easier when members of culprit-absent lineups can be easily rejected based on a conspicuous facial feature that distinguishes them from the culprit. This ease-of-rejection manipulation was reflected in parameter dA that was designed to capture the detection of the absence of the culprit; parameter dA was higher when the members of the culprit-absent lineups were easy to reject than when they were difficult to reject.Here we introduce the 2-HT eyewitness identification model that serves to measure latent processes underlying eyewitness identification performance. Before such a measurement model is applied to novel and unresolved research questions, it is important to empirically assess whether the model’s parameters sensitively reflect the processes they were designed to measure. This was done here by testing whether manipulations that can reasonably be expected to target culprit-presence detection, biased suspect selection, guessing-based selection and culprit-absence detection are sensitively reflected in the model’s parameters. In Experiment 1, parameter dP but not the culprit-absence detection parameter dA, whereas the manipulation of the ease with which culprit-absent lineups can be rejected influenced dA but not dP. Including a separate parameter for culprit-absence detection in a measurement model may help to stimulate thinking about how lineup procedures may help to improve the process of culprit-absence detection.It is a distinguishing feature of the 2-HT eyewitness identification model that it differentiates between two types of detection processes: the detection of culprit presence and the detection of culprit absence. Typical analyses of lineup data provide a single accuracy measure that is intended to reflect the witnesses’ ability to distinguish between culprit presence and absence. The 2-HT eyewitness identification model goes one step further by allowing to measure these two processes separately. The results of the present series of validation experiments suggest that these two types of detection processes can be manipulated independently of each other. The manipulation of exposure duration affected the culprit-presence detection parameter g reflects the process of selecting a lineup member based on guessing, while parameter b reflects the biased selection of a suspect who stands out from the fillers in an unfair lineup. This distinction is, of course, not entirely new, but the qualitative distinction between the two types of non-detection-based selection processes may be emphasized if they are distinguished in a measurement model. In the lineups used in Experiments 1, 3 and 4 and in the fair-lineup condition of Experiment 2, care was taken to create fair lineups by selecting fillers that resembled the culprits and the innocent suspects in hair color, hairstyle and stature and by making sure that the photographs of the fillers could not be distinguished from those of the culprit and the innocent suspect based on head size or lighting conditions. When lineups are fair, the biased-suspect-selection parameter b can be expected to be close to zero, which was confirmed by the present experiments. Experiment 2 shows that the 2-HT eyewitness identification model is also able to detect biased suspect selection in unfair lineups. We consider it a strength of this model that it allows measuring biased suspect selection in unfair lineups directly based on the eyewitness identification data of interest as opposed to having to rely on indirect data from a separate mock-witness task for determining lineup fairness82.Another distinguishing feature of the present model is that it differentiates between two forms of non-detection-based processes. Parameter 44 which, however, has been criticized for confounding the ability to distinguish between a culprit and an innocent suspect and response bias20. SDT-based analyses have sometimes shown equivalent performance of simultaneous and sequential lineups84 and sometimes a superiority of simultaneous over sequential lineups88. In all experiments reported here, the estimates of the culprit-presence detection parameter dP were slightly but consistently higher in the simultaneous-lineup conditions than in the sequential-lineup conditions. This difference in parameter dP was not statistically significant in any of the individual experiments, but when we combined the data from all experiments  from guessing-based selection (parameter g), which is conceptually similar to the distinction between sensitivity (d') and response bias (c) in SDT89. A more surprising finding is that, in the experimental paradigm used here, the estimate of the guessing-based selection parameter g was consistently higher in the sequential than in the simultaneous lineups. At first glance, this may seem unexpected because it has often been found that sequential lineups lead to more conservative responding than simultaneous lineups (e.g.83). In the present experiments, the higher tendency towards guessing that the culprit is in the lineup is already apparent at the level of the raw response probabilities: the mean rate of lineup identifications was consistently higher in the sequential lineups than in the simultaneous lineups . The most plausible explanation of the discrepancy of the data reported here to the data obtained in previous studies is that we explicitly followed standard police protocols by continuing the presentation of the sequential lineup even after an early identification63; only the last identification was used. By contrast, in many previous studies the only-the-first-yes-counts rule was used. Horry et al.62 have demonstrated that only-the-first-yes-counts instructions systematically reduce the rate of positive identifications in sequential lineups by discouraging participants from guessing. This is plausible at a psychological level: participants may well shy away from using their only identification option when they cannot know, at the time of their decision, whether better options will follow. These findings suggest that the standard police protocol applied here may induce a particularly pronounced tendency to select a lineup member based on guessing in sequential lineups: even if a lineup member was selected at some point, a later lineup member could still be selected if the witness came to the conclusion that the later lineup member provided a better match to their memory for the culprits.This convergence is not too surprising because the present 2-HT eyewitness identification model was designed to separate, among other things, culprit-presence detection , this is a comparatively small number of data points per participant. However, using a larger number of lineups would go against ecological validity because crimes with multiple culprits typically involve only a small number of culprits92.A limitation which the present approach has in common with other approaches is that it allowed us to estimate the different processes underlying eyewitness identification decisions only at the group level. Due to the small number of responses, it would be difficult to estimate parameters at the individual level98). A more general point is that MPT models are based on a threshold concept, assuming that observed behaviors are the result of discrete mental states rather than continuously distributed variables such as an assumed memory strength variable which is posited to underly recognition decisions in SDT-based approaches29. With respect to such recognition decisions, some researchers have argued that the threshold assumption of MPT models is inconsistent with the available empirical evidence100, but others have shown that both SDT-based and threshold models can account for recognition memory performance101. On the whole, it is important to note that all models are necessarily simplifications of the complexities of reality. The assumption of different mental states and of thresholds that have to be crossed to get from one state to another  is a simplification, but so is, for instance, the assumption of equal-variance, normal distribution of an assumed signal strength variable in models based on signal detection theory89. The question is not which set of simplifications is correct because all simplifications of the complexities of reality must necessarily be false. Instead, the question is which set of simplifications is sufficiently useful. Here we introduced a novel MPT model to the field of eyewitness identification research which we hope may turn out to be useful in that it makes it possible to measure the four latent cognitive processes of culprit-presence and culprit-absence detection, biased selection and guessing-based selection using the whole 2 × 3 data matrix of the typical lineup identification task (Table Another limitation is that the 2-HT eyewitness identification model in the present version accounts only for lineup identifications and rejections but not for confidence judgements. Accounting for confidence judgements would thus require an extension of the model (cf.The purpose of the present study was to introduce, and to provide an experimental validation of, the novel 2-HT eyewitness identification model that takes into account the full 2 × 3 structure of lineup data. The present results support the validity of the model for analyzing lineup data. Now that the model parameters have been demonstrated to sensitively reflect the effects of well-established and obvious manipulations of the latent processes underlying eyewitness identification decisions in simultaneous and sequential lineups, the model can be used in future studies to measure the latent processes underlying the witnesses’ decisions in lineups, for example, to test novel hypotheses.Supplementary Information.


Malnutrition is a determining factor of pediatric mortality and morbidity, especially in low and middle-income countries. Hospitalized children are at a higher risk of malnutrition. Several malnutrition screening tools have been used, among which STAMP, PYMS, and STRONGkids are valid tools with high sensitivity and specificity. The aim of this study was to compare these screening tools to find the best ones in identifying the risk of malnutrition in hospitalized children.This is a cross-sectional study performed on hospitalized children aged 1 to 16 years. The questionnaires of PYMS, STAMP, STRONGkids malnutrition risk assessment tools were filled. The weight for height and BMI for age Z-scores were calculated. The data were analyzed by SPSS. Sensitivity, specificity, positive predictive value, and negative predictive values of the risk scores based on weight for height and BMI for age Z-scores were calculated.P-value < 0.001), and BMI for age Z-score . Moreover, STRONGkids was found to be associated with weight for height Z-score .Ninety-three patients with a mean age of 5.53 ± 3.9 years were included. The frequency of malnutrition was reported as 26% and 39% according to weight for height and BMI for age Z-scores, respectively. A significant relationship was found between PYMS and Weight for height Z-score (The PYMS is a practical and beneficial tool in early identifying the risk of severe malnutrition in hospitalized patients. It is a suitable method for patients in our settings.The online version contains supplementary material available at 10.1186/s40795-022-00525-8. Screening Tool for the Assessment of Malnutrition (STAMP) was designed and validated in 2007 in the U.K for children of 2 to 17 years , 10.Pediatric Yorkhill Malnutrition Score (PYMS) introduced in 2008 .Screening Tool for Risk on Nutritional Status and Growth (STRONGkids), validated in 2007 in The Netherlands .Malnutrition in children is considered to be a determining factor of mortality and morbidity, especially in low- and middle-income areas. Malnutrition can cause serious financial and social problems in high-risk groups and thus impose a heavy economic burden on countries . MalnutrThese three screening tools have been used in outpatient and hospital settings and many studies have been conducted to investigate their sensitivity and specificity . These tDue to the importance of nutrition in hospitalized patients, discovering the ones susceptible to malnutrition could lead to early intervention and as a result, shorter stay in hospital and less morbidity. No study has yet tried to identify and analyze the patients at risk for malnutrition in our area. We decided to combine anthropometric indices and screening tools of STAMP, PYMS and STRONGkids. This association could inform us about individual nutritional requirements that need attention and intervention and the prevalence of malnutrition in admitted patients. By acknowledging the magnitude of the problem, we will be able to provide strategies for attending patients at-risk or affected by malnutrition. Besides, comparing the three malnutrition screening tools with conventional methods could yield in finding the ability of each one in discovering at risk patients.In a cross-sectional study, children from 1 to 16 years old who were admitted for more than 48 h in infectious, general, and surgery wards of a tertiary pediatrics hospital, Bahrami Children Hospital, Tehran, (Iran) in a 2-month period were included. All the patients admitted in the mentioned wards were included, literally all the patients admitted in each day, with different reasons for admission, were included (except for the exclusion criteria). Reasons for admission included gastroenteritis, urinary tract infections and pneumonia in infectious ward, diseases of GI tract and renal diseases in general ward, and patients admitted for surgeries like appendicitis and genitourinary abnormalities in surgery ward. Patients with immunodeficiencies and malignancies and patients who were admitted for less than 48 h were excluded. Informed consent was obtained from the patients’ parents upon admission. The aim of the study and the methods were explained well to the parents, and all of the parents were willing to participate. Height and weight were measured when patients were admitted and discharged by a trained nurse. The height of the children less than 2 years old was measured with a meter while lying down and the ones older than 2 were measured while standing up. Weight for children less than 2 years old was measured by the infants’ scale. The valid questionnaires of STRONGkids, STAMP and PYMS were filled for each patient by a pediatrics resident, who had been taught by studying the manuals of each risk score. The questionnaires can be found in the Supplementary Tables The world health organization (WHO) and Centers for Disease Control and Prevention (CDC) growth charts were respectively used to determine the nutritional status in children under 2 years of age and over 2 years of age, as recommended by the American Academy of Pediatrics (AAP) and the CDC . The antThe following formula was used to estimate the sample size, where P was considered as 0.32 based on a study by Spagnuolo et al. . Assuminn= (1.96)2 *0.32 *0.68 / (0.1)2 = 83The study adhered to the tenets of the Helsinki Declaration. All the information was kept confidential. No changes were applied to the treatment strategies due to this study. No additional costs were imposed on the family and no intervention was performed on the patient. Informed consent was obtained to participate from parents of minors. This study was approved by the ethics committee of Tehran University of Medical Sciences, Children's Medical Center, and the code of ethics was assigned to it: IR.TUMS.CHMC.REC.1397.092.th version. The descriptive data was reported by descriptive statistics . Chi square test and Fisher’s exact test were used to compare qualitative variables. For comparing quantitative variables, the student t-test was used for normal distribution and Mann–Whitney for non-normal. Pearson’s correlation coefficient was used to determine correlation. The P-Values less than 0.05 were considered significant. ANOVA test was used to analyze significant difference in malnutrition rates in the different wards. Sensitivity, specificity, positive predictive value, and negative predictive values of the risk scores based on weight for height and BMI for age Z-scores were calculated by crosstabulation.We analyzed the data by SPSS, 25A total of 93 patients were enrolled in this study, with a mean age of 5.53 ± 3.9 years, the mean height of 112 ± 20 cm, mean weight of 22.6 ± 14.4 kg and mean BMI of 16.58 ± 4.31 kg/m^2. The demographic data are summarized in Table Sixteen percent of the patients were admitted at the surgery ward, 41% at the general ward, and 43% at the infectious diseases ward. Patients’ data according to the admitted ward are shown in Table According to the weight for height Z-score, 66% of the patients were categorized as “normal”, 19% as “moderate malnutrition”, 7% as “severe malnutrition”, 2% as “overweight” and 6% as “obese”. BMI for age divided patients as 34% “normal”, 17% “moderately wasted”, 22% “severely wasted”, 12% “overweight” and 15% “obese”. No significant difference could be found in malnutrition prevalence between different wards by ANOVA test.PYMS scores ranged from 0 to 5, median 4:; STRONGkids scores ranged from 0 to 5, median:2, and STAMP scores ranged from 0 to 8, median:4.P-value < 0.001). It should be noted, though, that weight for height Z-score is only provided for children under 6 years old by CDC and WHO, and that is why a total of 53 patients were included in this analysis. Moreover, the STRONGkids score was significantly associated with weight for height Z-score when performing Fisher’s Exact Test . STRONGkids also revealed a relatively good ability to detect low-risk patients (79.5% or 31 patients) but performed poorly for high-risks (25% or 1 patient). STAMP did not show such significant relationships .We found a significant relationship between PYMS and weight for height Z-score  while the two other risk scores were not significantly related to the BMI for age Z-score. Relationships between PYMS and STRONGkids and weight for height z-score showed in the Table PYMS showed a significant relationship with the BMI for age Z-score , and was not associated with the two other scores, BMI for age or weight for height Z-scores.The median for LOS was 5 days (IQR 4 to 6) and it was shown to be associated with STRONGkids score , and according to BMI for age Z-score, 39.32% were wasted. Different studies over the past decade have reported malnutrition prevalence of 5.1% to 55.6%. . The preSTAMP, STRONGkids, and PYMS are valid questionnaires used as screening tools for malnutrition in different world regions –25. The Only a few studies have discussed or implemented nutrition screen tools in Iran. Khajavi et al. used STRONGkids to assess the nutritional status of pediatric cancer patients in 2020 . Moeeni An important feature of a screening score is the ability to predict clinical values such as length of stay in the hospital. According to our study, only the STAMP score was correlated with LOS. We could not find such a correlation between LOS and the two other scores, weight for height Z-score or BMI for age Z-score. In the study of Chourdakis et al. conducted in 2010–2011 on 2567 patients of twelve European countries, it was shown that children at high risk for the three risk scores  had a longer LOS than that of children at low risk .Regarding the efficacy of PYMS and STRONGkids use in children, we propose that children with moderate and high scores of PYMS and STRONGkids be referred to nutritionists. There is a gap in the literature in assessing the role of intervention by nutritionists in such groups; we suggest that more investigations are done in this area to help determine the use of PYMS and STRONGkids practically and permitting the children in developing countries to benefit from it.Malnutrition is a determining factor of pediatric mortality and morbidity, especially in low- and middle-income countries. Hospitalized children are at a higher risk of malnutrition. The PYMS and STRONGkids are practical and beneficial tools in early identifying the risk of severe malnutrition in hospitalized patients. They are suitable methods for patients in our settings.Future studies can be done with larger populations. We believe stronger correlations can be found between PYMS and STRONGkids and Z scores in larger studies. Also, now that we know these screening tools can be a reasonable tools for detecting malnutrition, studies can be done to validate this questionnaire for the use of Iranians, and also for assessing the ease of use of this tool in hospitals.Additional file 1: Supplementary Table 1. STRONGkids tool. Supplementary Table 2. PYMS tool. Supplementary Table 3. STAMP tool. Supplementary Table 4. A summary of reasons for admission.


GLIM-defined malnutrition is negatively associated with ADLs in older residents in IFMLC.Malnutrition is associated with poor functional outcomes in residents in long-term care facilities. The integrated facility for medical and long-term care (IFMLC) is a new Japanese long-term care facility where medical services can be provided. This study aimed to investigate the prevalence of malnutrition diagnosed based on the Global Leadership Initiative on Malnutrition (GLIM) criteria and its association with activities of daily living (ADL) in older residents in IFMLC. In this cross-sectional study of older residents, we diagnosed mild and severe malnutrition using the GLIM criteria and assessed ADLs using the Barthel index (BI). Multivariate regression analysis was used to investigate the relationship between BI score and GLIM-defined malnutrition. A total of 117 older residents  were analyzed in this study. The prevalence values of mild and severe malnutrition were 29% and 18%, respectively. Multivariate analyses for the BI score after adjusting for potential confounders showed that mild and severe malnutrition were independently associated with BI score (B = −6.113,  Malnutrition is a common problem often seen in older people receiving care in long-term care facilities (LTCFs). A systematic review found that 12–54% of the older people admitted to LTCFs were malnourished [Japan is the most aged country worldwide, and the burden of long-term care is continuously increasing. People aged ≥ 65 years accounted for 28.8% of the total population in 2020, and that proportion is expected to reach a peak of approximately 40% in 2040–2050 . The JapIn IFMLC, the association between the prevalence of malnutrition assessed using the Global Leadership Initiative on Malnutrition (GLIM) criteria and ADLs is unclear. The prevalence of malnutrition in LTCFs ranges from 1.5% to 66.5%, but definitions of malnutrition vary . To tackThis study aimed to investigate the prevalence of malnutrition diagnosed using the GLIM criteria and its association with ADLs in older residents in IFMLCs.This single-center study followed a retrospective cross-sectional design. The participating facility was the IFMLC Keiju Hatogawaka , which provided rehabilitation services based on the Japanese insurance system. The study was conducted from 1 to 30 November 2021, and the study participants were people aged ≥ 65 years who were admitted to an IFMLC during the study period. All clinical data were obtained during the study period. Residents who died during the study period and data missing were excluded.The basic policy of IFMLCs is to provide medical care management, nursing care, nursing care under medical supervision and other medical care, functional training, and daily living care to persons requiring long-term medical care based on the facility service plan . In IFMLThe comprehensive team of an IFMLC is composed of physicians, care managers, care workers, nurses, pharmacists, physical therapists, occupational therapists, and registered dieticians. In IFMLCs, two full-time physical therapists and two full-time occupational therapists provide rehabilitation to residents. Rehabilitation consists of joint range of motion practice, basic movement practice, standing and walking training, ADL training, endurance practice, muscle strengthening practice, and a mental functioning approach. Within the first 3 months after admission, intensive rehabilitation is provided with the addition of short-term intensive rehabilitation.In IFMLCs, one full-time registered dietitian provides nutrition care management. Nutrition care management is a Japanese long-term care insurance nutrition management method ,10, and As part of routine evaluations, all clinical information was recorded. Relevant information was extracted from the medical records and retrospectively examined in this study. All clinical data were obtained during the study period. These data included the demographic characteristics of the participants , affiliation before IFMLC admission, number of days spent at the IFMLC, primary diseases for facility admission, comorbidities, nursing care level (between 1 (mild) and 5 (severe) based on the assessment of care requirements) , weight The Malnutrition Universal Screening Tool (MUST)  was used2 in patients aged < 70 years and <20.0 kg/m2 in patients aged ≥ 70 years), and reduced muscle mass  < 34 cm in men and <33 cm in women) were employed to evaluate the phenotypic criteria. Weight loss was assessed using monthly weight measurements by nursing staff and caregivers using calibrated scales that allowed measurements while in a wheelchair. Data on weight loss in the past 6 months before admission were obtained by interviewing hospital staff, patients, or family members. BMI was calculated by dividing the body weight by the height squared (kg/m2). The cutoff value of the CC was taken from the sarcopenia diagnostic criteria for Asian populations [First, in all patients, weight loss (≥5% weight loss in the previous 6 months and ≥10% weight loss in >6 months), a low BMI , assimilation problems , and disease burden/inflammatory condition  were used to evaluate the etiologic criteria. Estimated energy requirements were established based on the activity level of each resident and the Dietary Reference Intakes for Japanese (2020) . The eneFinally, malnutrition was classified as severe in individuals with at least one of the following: severe weight loss (>10% weight loss in the previous 6 months or >20% weight loss in >6 months), severely low BMI, and severely reduced muscle mass (CC < 24 cm in men and <21.4 cm in women). Patients were diagnosed with severe malnutrition  based on the cutoff values reported previously for Asians ,19,20 weThe outcome was measured by determining independence in performing ADLs using BI . The totThe standard deviation of the BI for older patients in nursing homes in Japan was 28.5 in a previous study . At a stt-test, and Mann–Whitney U test. For BI over the study period, correlation analysis was performed using Spearman’s rank correlation coefficient. The factors that were independently related to BI were determined using a multivariate linear regression analysis. The covariates that were selected to adjust for bias were age, sex, primary diseases for facility admission, and the estimated time of rehabilitation dose [p-values of <0.05 were considered significant. JMP 11.2.1 software was used to conduct statistical analysis .Categorical variables are presented as numbers (percentages); parametric variables, as means ± standard deviation; and nonparametric variables, as medians (interquartile ranges (IQR), 25–75 percentile). The background characteristics for malnutrition using the GLIM criteria were compared using the chi-squared test, Fisher’s exact test, Student’s ion dose ,25,26,27A total of 126 residents fulfilled the inclusion criteria. Of these, two died during the study period, and seven had missing data, which were excluded. Finally, there were 117 participants in total  . The medp = 0.709). In addition, a comparison of the proportion between the severe malnutrition and nonsevere malnutrition groups showed no significant difference (p = 0.445). A comparison of the proportion of primary diseases at IFMLC admission between the mild and severe malnutrition and nonmild and nonsevere malnutrition groups revealed a significant difference (p = 0.001). The most common affiliation before IFMLC admission for both the mild and severe malnutrition groups was a hospital. Moreover, a comparison of the proportion between the severe malnutrition and nonsevere malnutrition groups showed a significant difference (p = 0.022). The mild and severe malnutrition groups had a significantly higher proportion of dementia and heart failure than the nonmalnutrition group. The mild and severe malnutrition group had a significantly higher proportion of dysphagia than the nonmild and nonsevere malnutrition group (p = 0.016). The severe malnutrition group had a significantly lower BMI, higher proportion of the high risk of MUST, and more dysphagia than the nonsevere malnutrition group .The characteristics of the participants at the time of the survey are shown in p = 0.035) and estimated time of rehabilitation dose . The univariate analysis revealed no significant difference in the BI between the groups according to sex and primary diseases for facility admission . The corp = 0.046) was independently associated with BI. GLIM-defined severe malnutrition  was also independently associated with BI  = −12.129 to −0.098,  with BI .We conducted a cross-sectional study of geriatric IFMLC residents to determine the prevalence of malnutrition assessed by the GLIM criteria or its relationship with ADLs. Two important findings from the study were related to nutritional support for older IFMLC residents. First, the prevalence values of mild and severe malnutrition were 29% and 18%, respectively. Second, GLIM-defined mild and severe malnutrition were associated with ADL. To our knowledge, this study is the first to mention the prevalence of GLIM-defined malnutrition in older residents in IFMLC or its association with ADLs.In this study, the prevalence values of mild and severe malnutrition were 29% and 18%, respectively. In previous studies, the prevalence values of older people with GLIM-defined malnutrition in hospitals, rehabilitation facilities, nursing homes, and communities assessed were 16.3–46% ,29, 52–6GLIM-diagnosed mild and severe malnutrition were associated with ADLs. A study reported the relationship between malnutrition and decreased functionality in older adults undergoing rehabilitation . SpecifiRehabilitation nutrition practices may be effective in improving malnutrition in IFMLCs. It is defined as the combination of rehabilitation and nutrition care . In thisThis study has several limitations. First, a causal relationship between malnutrition and reduced ADLs was unclear owing to the study design. The causal relationship between malnutrition and decreased ADLs must therefore be clarified through additional longitudinal research. Second, the outcomes of this observational study may not be as broadly applicable because it was restricted to a single IFMLC. Third, the small number of participants weakened the statistical power of this study. Thus, to overcome this limitation, multicenter prospective studies with a higher sample size are required.In this study, the prevalence values of mild and severe malnutrition were 29% and 18%, respectively, in older patients in IFMLCs. The severe malnutrition group had a significantly higher proportion of low BMI and reduced muscle mass than the nonmalnutrition group. Starvation and inflammation appeared to be the causes of malnutrition in IFMLC residents. Furthermore, GLIM-diagnosed malnutrition was independently associated with ADLs in this population. Therefore, we suggest assessing malnutrition with a disability early and managing all IFMLC residents properly. Rehabilitation nutrition practices are recommended for patients in IFMLCs with malnutrition and decreased ADLs.


COVID-19 has required researchers to adapt methodologies for remote data collection. While virtual interviewing has traditionally received limited attention in the qualitative literature, recent adaptations to the pandemic have prompted increased discussion and adoption. Yet, current discussion has focussed on practical and ethical concerns and retained a tone of compromise, of coping in a crisis. This paper extends the nascent conversations begun prior to the pandemic to consider the wider methodological implications of video-call interviews. Beyond the short-term, practical challenges of the pandemic, these adaptations demonstrate scope for longer-term, beneficial digitalisation of both traditional and emergent interview methods. Updating traditional interview methods digitally has demonstrated how conversion to video interviewing proves beneficial in its own right. Virtual focus-group-based research during COVID-19, for example, accessed marginalised populations and elicited notable rapport and rich data, uniting people in synchronous conversation across many environments. Moreover, emergent interview methods such as the Grid Elaboration Method  demonstrated further digitalised enhancements, including effective online recruitment with flexible scheduling, virtual interactions with significant rapport, and valuable recording and transcription functions. This paper looks beyond the pandemic to future research contexts where such forms of virtual interviewing may confer unique advantages: supporting researcher and participant populations with mobility challenges; enhancing international research where researcher presence or travel may be problematic. When opportunities for traditional face-to-face methods return, the opportunity for virtual innovation should not be overlooked. The COVID-19 pandemic disrupted our everyday lives . Beyond Interviewing is the most prevalent mode of data collection in qualitative research . While rHowever, as video-call platforms grow more advanced and widely used, methodological discussion of video interviewing has emerged in qualitative research . This inThe pandemic prompted global uptake of remote data collection. Recent qualitative research features telephone interviewing , Email iHowever, most recent discussions of video interviewing have focussed on its practical, technical and ethical protocols and implications. Reflections have included practical strengths and weaknesses of each video-call platform; logistical considerations for optimising uptake and security in online recruitment; procedural requirements for ensuring informed consent and data privacy in virtual interview settings; recommendations for supporting IT literacy for researchers and participants; discussions of call-recording functions for post-interview transcription purposes; and technical suggestions for maximising video-call quality . UnderstThis paper argues that video interviewing should be considered less as an adaptive compromise within pandemic restrictions and more as an opportunity for longer-term methodological progress. While most discussions of recent technological adjustments to data collection are generalised, this paper focusses on specific methods. It scrutinises digital innovations: firstly of traditional methods, such as focus groups, and secondly of emergent semi-structured methods, such as the Grid Elaboration Method.Adaptations to longstanding qualitative methods during COVID-19 prompted beneficial updates to their flexibility and efficacy. Focus groups provide an instructive example. These had started to shift online before the pandemic , but werThe pandemic provoked refreshed exploration of video interviewing, particularly for focus groups. Some, very recent studies, meanwhile, have contributed insights into the methodological advantages of conducting video-call focus groups. Two online video focus-group studies with low-socioeconomic-status African American participants yielded access to marginalised, underrepresented populations by facilitating travel-free contact with geographically dispersed participants . These sVirtual-focus-group participants also reported enjoying having their voices heard and connecting with others during the pandemic . In emerFurthermore, technology-assisted transcription tools promise particular advantages for focus groups. Capturing precise dialogue requires verbatim transcription, yet determining which participant is speaking from group recordings containing overlapping voices is difficult . Video-cCOVID-19 has also catalysed virtual evolutions of emergent qualitative methods, exemplified by the Grid Elaboration Method GEM; . The GEMThe GEM exemplifies more specialised qualitative methods, where employing procedures in person appears preferable . Yet, itAnother study transposed the full GEM interview format online, through real-time video calls. It examined how people with chronic pain conceptualized it, inviting participants to fill in paper grids remotely . ParticiThese studies demonstrate how even complex, emergent methods could be productively digitalised during the pandemic. Their challenges and successes offer a lens for further exploring the methodological strengths of virtual interviews using the GEM and other qualitative methods. Several strengths are outlined below: namely, virtual methods’ accessibility and recruitment viability, scope for interview rapport, data richness and transcription efficiency.Firstly, virtual interviewing has widened geographic access to participants . AlthougPre-interview logistics are also highly feasible online. This corroborates literature discussing virtual alternatives to gaining informed consent while preserving data confidentiality . ParticiVirtual interview interactions themselves have demonstrated significant emulation of natural conversation . Early rAdditionally, data elicited via video calls have demonstrated substantial richness akin to that gained in face-to-face contexts. Factors influencing the quality of the data collection, such as systematicity in the application of interview procedures and participant-researcher real-time rapport, can be fulfilled through virtual methods. Participant choice regarding the setting in which they are interviewed, provision of clear instructions and establishment of online rapport have facilitated extended elaborations for detailed thematic analyses .Lastly, transcription of interviews can be supported by audio files captured directly from the video-call platform. This increases clarity of recordings, channelling audio input directly from participants’ electronic devices. Innovative transcription tools hold significant potential for improving researchers’ capacity to conduct interviews in varied formats and transcribe them reliably. Platforms including Zoom, Microsoft Teams and Skype provide automated transcription functions.Virtual research during COVID-19 has demonstrated that traditional and emergent qualitative methods can flourish remotely. Yet, this digitalisation can be further innovated. Across all virtual qualitative research methods, researchers and participants could turn off their cameras while participants are completing prompts to be used in later interview stages—for example, when filling in grids in the GEM. In work meetings, switched-off cameras may cause less engagement , but wheMoreover, in virtual group interviews, hand-raising functions embedded in video platforms can enhance how participants interact and share space in discussions. That said, group dynamics pervading face-to-face interviews should still be considered online: such as whether men may on average exercise greater confidence in hand-raising or speak for longer once called on . ResearcSecondly, video platforms such as Zoom and Microsoft Teams provide emerging options for real-time transcriptions displayed on screen. This may support researchers and participants to understand each other if either or both are speaking in non-native languages or have hearing impairments – more effectively than in person – by supplementing auditory information with text.During COVID-19, interacting online became the setting for navigating stress, loneliness, and uncertainty of lockdowns. In this uniquely digitised context, being interviewed on video platforms may have felt increasingly familiar. It has opened new opportunities for accessibility and representation in research, and prompted valuable methodological innovations for further, post-pandemic research.While digitalising qualitative methods allowed research to function through global disruptions, it revealed a wider range of longer-term advantages. Recent discussions of virtual interviewing are still limited to coping strategies against social-distancing hindrances . Having In a pandemic, researchers and participants alike are prevented from travelling to face-to-face interviews by social distancing measures. Yet, at any time, some populations may have short- or long-term mobility difficulties. This includes people with specific physical disabilities, for whom video calls may be significantly more convenient than travelling to research institutions, hosting researchers, or visiting participants . Some olVirtual interviewing is also promising regarding international research. COVID-19 has already prompted greater online communication between international research communities, increasing the collective efficacy of global collaborators  and inclBeyond providing more ethical and equitable interview settings for numerous social groups and practical advantages for researchers, virtual innovations can also enhance qualitative research from a theoretical perspective. Video calls may allow employed participants to fit interviews more easily into their working days. Furthermore, allowing participants to engage from their homes grounds them in environments where everyday associations might not be disrupted by unfamiliar settings . This maUse of virtual therapies is increasing, alongside increased evidence for their efficacy and desirability , and quaThese theoretical benefits satisfy overarching principles for rigour, accountability and transparency within qualitative research . DigitalThese nascent modes of virtual research still carry limitations warranting consideration. Primarily, novel technologies require updated ethical scrutiny . While sThere are also some methodological drawbacks. Using automatic transcription may render some researchers less familiar with their data than if transcribing manually, where deciphering interviews supports researchers to absorb their content. However, manual transcription may soon become outdated, given the numerous automated transcription applications already available. Research projects have also historically hired transcribers, so researchers are usually not embedded in their data at the transcription stage anyway. Also, counter to the strengths raised above, since researchers do not visit in person the contexts under investigation, they should embed themselves in the culture in alternative ways, such as exploring regions using wider literature, news media, talks and even interactive virtual maps . AdditioVirtual interviewing presents multiple commonalities with face-to-face methods, supporting its methodological legitimacy. It also displays key differences – both improvements and limitations – which merit concise delineation.Advertisement, recruitment and interview organisation are largely online in both cases. Core interviewing approaches – from semi-structured interviews to specialised procedures – remain unchanged online. Facial and vocal cues are retained in video calls, and rapport can be developed. Participants express enjoyment and feeling heard in both settings. Focus group dynamics are analogously pervasive online. Both settings accommodate interview recording.However, virtual interviewing can exclude communities and individuals without technological competence or access. Consent is more difficult to obtain, and data confidentiality requires caution. Body language is less observable, and participants may fatigue faster or face home distractions. It is harder to view participants’ written prompts in specialised methods requiring them.Yet, virtual interviewing provides enhanced recording capacities, with clearer audio capture and automatic transcription, including real-time captioning. It offers instant messaging and screen-sharing options, and recordings can be video as well as audio. Interview scheduling is more flexible, and travel expenses are removed. It enables access to remote communities, regions where travel is prohibited and participants with impaired mobility. It also supports greater ecological validity, removing institutional settings and reducing researcher presence.Research communities around the world have rallied to support global efforts to understand and overcome the COVID-19 crisis. These concerted efforts have contributed significant innovation in research methods to surmount unprecedented barriers. Such innovation need not be lost when these barriers subside. The pandemic has encouraged increased global interest in virtual qualitative research methodologies already emergent in the literature: particularly, video interviewing.Yet, discourse surrounding this innovation has retained a tone of compromise. This paper seeks to shift the emphasis from coping to opportunity and progress. Growing evidence from cutting-edge research suggests that virtual interviewing can thrive in its own right. Traditional methods such as focus groups and emergent methods including the GEM can find refreshed efficacy in virtual formats. As virtual studies proliferate across varied research contexts, they pose unique promise for greater access, efficiency and ethicality.In the same way that virtual therapy has not supplanted face-to-face therapy conventions, so digital research need not supplant in-person research conventions; it can complement them and simultaneously yield wider access . Traditi


SARS-CoV-2 infection entails neuroinvasive, neuroinflammatory, and treatment-related features accounting for cognitive deficits in COVID-19-recovered patients. Although screening for such dysfunctions in this population is considered clinically relevant, contributions to cognitive phenotyping including premorbid and disease-related confounders are scarcely represented. This study thus aimed at describing the cognitive outcome at the function-/domain-level of post-infectious SARS-CoV-2 patients being already at risk (RCD +) or not (RCD −) for cognitive decline.N = 54, 34, 28, and 28 patients, respectively).Fifty-four COVID-19-recovered individuals were classified as either RCD + or RCD − according to medical records. The Mini-Mental State Examination (MMSE), Addebrooke Cognitive Examination-Revised (ACE-R), Frontal Assessment Battery (FAB), and Attentive Matrices (AM) were administered  MMSE scores was 24.3% in RCD + patients and 5.9% in the RCD − group. ACE-R-total below cutoff scores were less frequent . Abnormal performances at the FAB an AM were respectively detected in 18.9% and 8.1% of RCD + patients and 0% and 11.8% of the RCD − group. Within the ACE-R subtests, those assessing orientation, attention, and fluency were the most frequently impaired in both groups. Disease-related variables were mostly unassociated with cognitive measures.Both RCD + and RCD − COVID-19-recovered individuals might show cognitive deficits within the dysexecutive-inattentive and amnesic spectrum. Non-instrumental, executive/attentive dysfunctions are predominant in this population and can be detected by both screening and domain-specific psychometric tests—although the latter might be more sensitive in RCD − patients. SARS-CoV-2 infection entails neuroinvasive properties and systemic neuroinflammation that likely account for cognitive deficits occurring in COVID-19-recovered patients –7.Screening for cognitive dysfunction in this population has been suggested as clinically relevant regardless of the occurrence of neurological signs/symptoms , as it dExecutive/attentive and episodic-memory dysfunctions have been highlighted as predominant features of the cognitive profile of post-COVID-19 patients –13. HoweThereupon, this study aimed at describing the cognitive outcome of post-infectious SARS-CoV-2 patients at the function/domain level by separately addressing those being already at risk (RCD +) or not (RCD −) for cognitive decline.N = 54 post-infectious SARS-CoV-2 patients referred to IRCCS Istituti Clinici Scientifici Maugeri of Milan  between 2020 and 2021 were retrospectively collected ; and “moderate-to-severe” (requiring either non-invasive ventilation or ICU admission).Based on current guidelines , COVID-1This study received approval by the local Ethics Committee  and was conducted in accordance with the Declaration of Helsinki.N = 28 patients—attentional matrices (AM) [Screening measures of cognitive efficiency—Mini-Mental State Examination (MMSE) , 21; Addces (AM) .SPSS 27  was used to analyze data.In order to draw clinical judgments, MMSE, ACE-R, FAB, and AM scores were standardized according to the equivalent score (ES) method . The ES α levels were Bonferroni-corrected (αadjusted = 0.025). Missing data due to imputation issues were excluded pairwise.Due to data distributions being often skewed and overdispersed, associations/predictions of interest were explored through non-parametric tests. When running analyses separately for RCD + and RCD − patients, Table N = 37) and RCD − (N = 17) patients were balanced for all background and clinical variables .RCD + (CD − N = 7 patientN = 54) was 24.3% in RCD + patients and 5.9% in the RCD − group. ACE-R-total below-cutoff scores (N = 34) were less frequent . With respect to the FAB an AM, impaired performances were respectively detected in 18.9% and 8.1% of RCD + patients and 0% and 11.8% of the RCD − group. Within the ACE-R subtests, the ACE-R-AO and ACE-R-F were found to be the most frequently impaired in both RCD +  and RCD −  patients. The highest prevalence of defective performance among FAB subtests was found for the FAB-1 in both groups .Prevalence of defective MMSE scores (H(3) ≤ 6.49; p ≥ 0.078), ICU admission , steroidal treatment , and co-occurring infection  were detected on adjusted cognitive scores—with the exception of co-occurring infections on ACE-R-F  and ICU admission rates on FAB-3 scores  in RCD − patients. More specifically, ICU-admitted RCD − patients reported higher FAB-3 scores (Mdn = 5.96) vs. non-ICU-admitted ones (Mdn = 4.05), whereas RCD − patients suffering from co-occurring infections featured higher scores on the ACE-R-F (Mdn = 11.9) when compared to those who did not (Mdn = 7.77).In both groups, no effects of disease severity , except for disease duration being inversely related to ACE-R-L scores in RCD − patients (rs(11) =  − 0.71; p = 0.015) and positively with ACER-R total (rs(11) = 0.61; p = 0.002) and ACE-R-F (rs(11) = 0.51; p = 0.013) scores in the RCD + group.Disease duration and time from onset were mostly unassociated with cognitive measures in both groups  and those who did not (RCD -). According to previous findings , 14, in The present results are also consistent with the notion of non-instrumental, dysexecutive-inattentive dysfunctions being predominant in this population , 27, 28.Attention deficits—as revealed by both I- (ACE-R-AO) and II-level measures (AM)—were detected in both groups . InteresNotably, besides the ACE-R-F subtest, the only other ACE-R subtest yielding defective performances in RCD − patients was related to memory functions (ACE-R-M). In this regard, both subjective memory difficulties and objective memory deficits are among the most frequently reported cognitive symptoms by COVID-19-recovered individuals , which a-infectious SARS-CoV-2 patients.The present study also provides practitioners with evidence about the effectiveness of the ACE-R, FAB, and AM in detecting cognitive dysfunctions in postAs to RCD + patients, the descriptive finding of an overall greater prevalence of defective cognitive performances when compared to the RCD − group was expected, as the former showed neurological/psychiatric conditions which might have already impacted on their cognitive functioning. However, it is noteworthy that, when addressing adjusted scores instead of prevalence values, no differences were detected in cognitive measures between these two groups. Although this finding should be regarded with extreme caution due to the small sample sizes, it is reasonable to postulate that, within the present RCD + cohort, patients did not present, at least at the group level, with cognitive dysfunctions of such a severity that would have allowed for them to be discriminated form RCD − ones.Finally, poorly interpretable findings raised on the interplay between cognition and disease-related features in COVID-19-recovered individuals . However, the present data pointing out at better executive outcomes (FAB-3) in ICU-admitted patients and at a greater language involvement (ACE-R-L) in patients with higher disease duration, consistently with previous contributions , 30. SucThis study does present with several limitations. First, the sample size is relatively small and data were retrieved from a single clinic. Moreover, since this was a retrospective investigation, not all cognitive measures herewith addressed were available for all patients. Both these elements may to an extent limit the external validity of the present findings. Furthermore, a restricted range of cognitive tools (mostly screening ones) were addressed within this study: further investigations are thus desirable that take into account a thorough cognitive battery.In conclusion, the present work shows that COVID-19-recovered individuals regardless of the pre-existence of risk for the cognitive decline might show cognitive deficits within the dysexecutive-inattentive and amnesic spectrum. Non-instrumental, executive/attentive dysfunctions are predominant in this population and can be detected by both I- and II-level psychometric tests—although the latter might be more sensitive to such impairments and should thus be preferred in patients who are not already at risk for cognitive decline.