Robots that can exhibit human-like behaviour, build social relationships with humans, and carry out social interaction that can be considered to have a social presence. Measuring the social presence of robots can provide an important basis for optimizing the design of robots’ human-like behaviour, but until now, there has been no research and discussion on the presence of robots from the perspective of social interaction. Based on related studies, such as the theory of the presence of interpersonal interaction society, the mechanism of network social presence measurement and human–computer interaction, this study constructs a theoretical model of robot social presence, and develops corresponding measurement scales with five dimensions, namely, the presence, attention distribution, interactive expression and information understanding, perceived emotional interdependence, and interaction behaviour perception. The 5-dimensional robot social presence model was finalized, and a 17-question questionnaire scale was developed. The validation of the model and the development of the scale includes one expert assessment (involving three experts), one user interview (involving five interviewees), and two questionnaire surveys . The final model shows good reliability of the measurement questionnaire, high inter-factor correlation, high model fit, high internal consistency of the dimensions, high reliability of the scale, and high convergent validity of all dimensions. This study provides a theoretical basis for the study of robot social presence and human–robot collaboration and provides a reference measurement tool for social robot-related development and research. Social robots are automatic procedural agents that can automatically generate content and actively participate in human social interactions2, have anthropomorphic social attributes and social behaviours, and can interact with users emotionally3, assist them in decision making4, provide companionship to reduce the number of users5, or provide companionship to reduce user isolation5. Social robots can be assistive tools for the elderly6 and people with physical or cognitive disorders8, help children with autism to improve their social skills9, and replace human customer service for customers10. For example, Estee Lauder has developed a chatbot, named No. 6 Mortimer, that can help customers select the ideal product  with the bodies of others, is a physical and objective presence caused by the inevitable meaning of human physiological attributes. When an individual senses the presence of others, the sense of co-presence in the same environmental space allows the individual to perceive the social presence of the other28. Zajonc40 argues that when in the presence of others, the individual’s attention changes in response to the presence of others, and the level of ego drive or arousal increases,thus, cognitive processing abilities change and lead to increased or decreased individual task performance.Most current scholars consider social presence to be multidimensional, including Gunawardena28 proposed that when an individual senses the interaction intention or emotional state of an interacting object, the other person is considered to have a significant social presence because, according to the definition of social presence, if an individual realizes a significant degree of interaction or an interpersonal relationship is generated by an interaction, then it means that the individual has felt the corresponding degree of social presence33. Many previous social presence-related models include a mental state dimension. For example, in the three-dimensional social presence model proposed by Gunawardena19, two dimensions express psychological states, including affect or emotional expression and psychological belongingness, and in the three-dimensional social presence model proposed by Nowak39, one dimension expresses the psychological state, namely, a deep sense of psychological involvement. Biocca28 further considered emotion as one of the important components of social presence. The mental state dimension further includes perceived message comprehension, level of attention allocation, emotional comprehension, and degree of emotional dependence. Among them, perceived message comprehension refers to an individual’s ability to understand the message of the interacting object and the individual’s perception of his or her ability to understand the message26. The level of attention allocation is the amount of attention that the individual allocates to the interaction object and the perceived amount of attention that the other individual allocates to the individual itself. The salience of social presence can be demonstrated by the level of attention allocation41. Affective comprehension refers to the individual’s ability to understand the emotional and attitudinal states of the interaction object, as well as the perceived ability of the other individual to understand the individual’s own emotions, which also includes empathy28. The degree of affective dependence refers to the extent to which the individual’s mental and attitudinal states influence and are influenced by the mental and attitudinal states of the interaction partner26.The second dimension of mental state presence is the person’s subjective perception, which includes the expression and understanding of emotions, changes in attention allocation, and changes in mental states. Biocca42 argues that in addition to spatial copresence with others and judgements of others’ identities, intentions, and attention, social presence also includes deeper interactive perceptions, such as a sense of behavioural participation. Biocca and Harms28 also suggest that behavioural participation is one of the important components of social presence, including behavioural interaction, mutual aid, and interdependence. The two scholars later further defined this dimension as the behavioural interdependence ability, i.e. the degree to which the behaviour produced by an individual influences and is influenced by the behaviour of the interacting object29.The third dimension is presence with behavioural involvement and interaction, which is the deeper perception brought about by the mutual influence of the behaviours of the interacting parties. Kuwabara43. Different from the physical presence dimension, the conscious presence-perception dimension emphasizes feeling, while the former focuses more on objective presence. Harms et al.26 argue that social presence should include symmetrical perceptions corresponding to the individual’s subjective perceptions, i.e. whether the individual perceives the presence of the interacting object at the same level of consciousness as the other individual and whether the other individual perceives the presence of the interacting object at the same level as the corresponding individual. Nario-Redmond et al.44 describe presence perception in terms of co-presence awareness and argue that it is an important constituent dimension of social presence. Co-presence awareness refers to the degree to which an individual believes that the interaction object is not isolated, the degree to which the individual is peripherally or focally aware of others, and the individual’s sense of the degree to which others are peripherally or focally aware of him or her. Table In addition to these three dimensions, some scholars have proposed that social presence also contains a conscious presence-perception dimension, i.e. the individual’s experience of perceiving and being perceived by others as felt through interaction45 and the intimate connections that individuals make with others26, and robots can also generate social presence properties when artificial intelligence technologies help them exhibit social attributes and human-like social behaviours and establish social relationships with humans46. Social robots can exhibit social capabilities, such as sensing emotions, conversational communication, learning recognition, establishing or maintaining social relationships, motor behaviour, and personality expression15, and possess anthropomorphic behaviours, such as self-determination, care, companionship, communication, and entertainment, based on the environment and, thus, can be considered to have social attributes. Robots with anthropomorphic behaviours and social attributes can socially interact with and be perceived their social presence by human individuals. The ultimate goal of human–robot interaction is to provide a strong social interaction experience14, and the higher the level of social presence, the more likely the robot is to be perceived as a real social character, the richer the sense of the human interaction experience15, and the more positive the perception of robots. However, there is a lack of theoretical research about robots’ social presence. The present study has been conducted on the social presence theory of human interaction16 and measurement models , and other related studies to explore robots’ social presence from the perspective of social attributes.Social presence in interpersonal interaction arises from the direct interactions that individuals establish with others in the environmentThis study proposes a definition of robots’ social presence, which means that in the case of direct interaction between a human individual and a social robot with artificial intelligence, if the individual can perceive the same or similar feelings as when interacting with a real human, then the latter is considered to have social presence. Based on the four dimensions of social presence in human interaction summarized above, this study proposes a six-dimensional model for robots’ social presence, including attention allocation, interactive expression and information understanding, emotional understanding and expressiveness, perceived emotional interdependence, perceived degree of interaction behaviour, and presence. The original model of the study is shown in Fig. 41 refers to the amount of attention an individual can allocate to the robot and the amount of attention the individual perceives the other person is allocating to him or herself. Interactive expression and information understanding47 refers to the degree to which the individual perceives that both parties to the interaction understand the information in the process. Emotional understanding and expression48 refers to the ability of the individual in the interaction to understand the emotions and expressions of the robot and the ability of the individual to perceive the expression of the robot’s emotional and attitudinal states. Perceived emotional interdependence49 refers to the extent to which an individual’s emotional and attitudinal states influence and are influenced by the robot’s emotional and attitudinal states. Furthermore, social presence in interpersonal interaction includes two main dimensions, i.e. physiological presence and copresence perception26. In the context of human–robot interactions, the former refers to the individual’s ability to perceive the copresence of the robot in a space after interacting with it through vision, hearing, etc. while the latter refers to the significant presence of the robot as perceived by the individual subjectively and consciously through the interaction. Of these, the latter is the basis for the generation of the former, and it is difficult to distinguish between the two, so this study proposes the presence dimension, which measures both the individual’s perception of the presence and copresence of the physical dimension of the social robot in the interaction.Of these, the first four dimensions directly reference the secondary dimensions of the mental state social presence dimension in human–computer interactions. Because the key factors affecting the level of attention allocation, perceived message comprehension, emotional comprehension, and degree of emotional dependence in the psychological dimension of human–robot interaction differ, all four secondary dimensions are included as separate subdimensions of the robots’ social presence. Of the human–robot interaction dimensions, attention allocation50, which refers to the degree to which individuals perceive behavioural influence on and are influenced by the robot’s behaviour.Finally, this study directly references the presence dimension of behavioural engagement and interaction in interpersonal interactions and proposes the perceived degree of interaction behaviour dimension of robots’ social presence26, interpersonal social existence theory51, and structural dimensions of existence and reality judgment in virtual environments52. We combined items with similar meanings and 105 questions were obtained. After evaluation by three experts, we selected 24 questions related to human–robot interaction from the scale of human interaction and virtual interaction. Finally, according to the advice of experts, the 24 questions were divided into 6 dimensions to form the initial scale shown in Table Using social presence as the key word, we retrieved 611,000 related papers in Google Academic database and screened out the scales related to social presence, such as Network psychometric scaleBased on the theoretical framework of robots’ social presence, this study will design and develop the corresponding scales. The scale development construction consists of three studies. The first study used the expert evaluation method and user interviews to evaluate and revise the original model and the measurement scale to form the final model Fig.  and the In this study, first, three experts were invited to evaluate the original model and scale validity of robots’ social presence. They were from the School of Economics and Management and the School of Information Science and Technology of Beijing University of Chemical Technology, including one expert in the field of artificial intelligence, one expert in the field of psychology, and one expert in the field of sociology. Each expert was required to evaluate the definition and model of robots’ social presence proposed in this study, the expert tested the face validity of the scale to measure the relationship between items and Robot’s Social Presence, and to review the content validity and discriminant validity of each dimension of the scale individually.54, according to the uncanny valley theory55, humans do not like robots with overly anthropomorphism in appearance, so common social robots usually have a low degree of facial anthropomorphism and a single expression design, which leads to the human recognition of robot emotions being generally low, and the emotional expression ability of social robots cannot be effectively measured by questionnaires. At the same time, the interpretation of emotional expressions is not the main factor affecting the social presence of robots under current technological conditions and human acceptance levels. Therefore, the discriminant validity of the dimension of emotional understanding and expression ability and the two dimensions of perceived emotional interdependence in the original model is low. Respondents have difficulty distinguishing between the meanings of the questions in the two dimensions due to the limitations of the technical conditions and personal experience, and a dimensional merger is recommended.The three experts believe that the definition proposed in this study provides a complete overview and representation of robot social presence in human–robot interaction from the following three perspectives: interaction object, interaction mode, and interaction outcome. However, currently humans understand the emotional expression of social robots mainly through recognizing facial expressionsAfter that, this study invited five respondents with experience in using social robots to conduct structured interviews, they were asked to fill out the original questionnaire based on their own experience and to assess whether the language of the questions was clear and precise enough, whether there was repetition or conflict in the understanding of the question items, and whether the meaning was concise and easy to understand. The questions on the comprehension and expressiveness dimensions were questioned. They indicated that the social robots they could come across in their daily lives generally had fixed facial expressions, showed a single type of emotion, and fluctuated to a small degree, and the anthropomorphic emotional expression felt during interaction was not significant, so they rarely paid attention to the robot’s emotional expression, and it had a very limited impact on the interaction effect. On the other hand, two respondents believed that their needs regarding the hardware functions of social robots were greater than their needs for emotional communication, and they could not imagine a scenario of interacting with a social robot with anthropomorphic emotions and language expressions, so they could not make accurate judgements, and this dimension in the questionnaire was not an important dimension for assessing the social presence of robots. The results of the expert evaluation and user interviews were combined, related questionnaire items on the emotional understanding and expression ability dimension was removed from this study, and a 5-dimensional modified model of robots’ social presence and the corresponding measurement questionnaire were proposed, as shown in Fig. Based on the modified model and the corresponding scale-modified questionnaire, this study conducted a questionnaire survey. The questionnaire contained two parts. The first part was demographic questions about the respondent’s gender, age, and whether they had experience with social robots, and the second part focused on the modified scale of robots’ social presence with 19 questions, measured using a 5-point Likert scale, where 1 = strongly disagree and 5 = strongly agree. The questionnaire took approximately 2 to 5 min to complete.In this study, 200 questionnaires were distributed, and 172 questionnaires were returned. Of these, 93 valid samples (i.e. responses with relevant experience) were obtained for this study based on whether or not they had experience with social robots, including 19 male individuals 20.43%) and 74 female individuals (79.57%), one of them (1.08%) under the age of 18, 76 individuals (81.72%) between the ages of 18 and 25, 13 individuals (13.98%) between the ages of 26 and 30, 2 individuals (2.15%) between 31 and 40 years old, and 1 individual (1.08%) over 40 years old. The specific basic information of the respondents is shown in Table 3% and 74All methods were performed in accordance with relevant guidelines and regulations. All study protocols were approved by the institutional review board of the Ethics Committee of Beijing University of Chemical Technology. Informed consent was obtained from all the participants.2 = 879.382, df = 171, p < 0.01). The results of the Kaiser–Meyer–Olkin index analysis indicated a high correlation between the factors (KMO = 0.807 > 0.800), which indicates that the questionnaire data are suitable for exploratory factor analysis56. The exploratory factor analysis results showed that the five dimensions of presence, attention allocation, interactive expression and information understanding, degree of emotional-attitudinal interaction, and perceived dimension of interaction behaviour in the modified model explained 66% of the variance, indicating the strong explanatory validity of the model57. The specific analysis results are shown in Table Exploratory factor analysis (EFA) was first performed to confirm the discriminant validity of the model dimensions. The results of the consistency analysis indicated good reliability of the questionnaire . The results of Bartlett’s sphericity test indicated the presence of a common factor  = 1.333, root mean square error of approximation (RMSEA = 0.061), Tucker-Lewis index (TLI = 0.928), comparative fit index (CFI = 0.943), fitness test (GFI = 0.845), root mean squared of asymptotic residuals (RMSEA = 0.061), and standardized residuals mean squared (SRMR = 0.087). Moreover, the canonical fit index (NFI = 0.812) and the adjusted goodness-of-fit index (AGFI = 0.781) were close to the acceptable range. These results indicate that the model fit is good59, but further optimization is needed. The results of the specific analysis are presented in Table Based on the results of the validation factor analysis, the results of each model fit metric had acceptable fit, including chi-square/degree of freedom  = 0.431 < 0.5, and the factor loading of the item “I think it is difficult for social robots to understand me” was 0.499, which was lower than 0.5. Combined with the results of the structured interview in Study 1, it was concluded in this study that this question item differed significantly from the other questions in the dimension, so it was removed from the scale and not explored in future data analyses.The construct reliability (CR) values for the other four dimensions of the model were greater than 0.8, indicating that the questions in the dimensions explained the variable in a largely consistent manner. The average variance extracted (AVE) values were all greater than 0.5, indicating that the model had good convergent validity across the dimensions.2/df = 1.275, RMSEA = 0.055, TLI = 0.946, NFI = 0.841, and CFI = 0.959, as shown in Table Adjusting for the two items resulted in the final robots’ social presence scale for this study, which contained 17 items. The results of the validation factor analysis indicated that the model had a high fit and outperformed the modified model, including χTo verify the stability of the final robots’ social presence scale across samples, the study was conducted again with 719 questionnaires. Again, 494 valid questionnaires were obtained based on whether they had experience with social robots as a screening criterion. These included 174 male respondents (35.22%) and 320 female respondents (64.78%). Seven respondents (1.42%) were under 18 years old, 323 respondents (65.38%) were between 18 and 25 years old, 46 respondents (9.31%) were between 26 and 30 years old, 42 respondents (8.50%) were between 31 and 40 years old, 38 respondents (7.69%) were between 41 and 50 years old, and 38 respondents (7.69%) were between 51 and 60 years old (7.69%).2 = 3576.290, df = 136, p < 0.01). The results of the Kaiser–Meyer–Olkin index analysis indicated a high correlation between the factors (KMO = 0.883 > 0.800). These results indicate that the questionnaire data are suitable for exploratory factor analysis.The results of the consistency analysis indicated the good reliability of the questionnaire . The results of Bartlett’s sphericity test indicated the presence of common factors (χThe results of the exploratory factor analysis indicated that the model extracted five factors, and each factor contained question items consistent with the final robots’ social presence scale. The specific results of the analysis are shown in Table 2/df = 2.160, RMSEA = 0.048, TLI = 0.928, NFI = 0.939, AGFI = 0.926, SRMR = 0.052, CFI = 0.966, GFI = 0.950. The results of the specific analyses are shown in Table The results of the validation factor analysis showed that the model had a high fit, including χInformed consent was obtained from all the participants and/or their legal guardians.In this study, the properties and social presence of robots from the perspective of social interactions are explored, the definition of robots’ social presence is given, and model construction is conducted. The previous studies on social robots have focused on the intelligence level and functional properties of robots, and few social properties of social robots have been explored from a social perspective. In the social presence-related studies, researchers have mainly explored social presence in interpersonal interactions, and few studies have focused on the social presence properties of robots with artificial intelligence. This study synthesizes previous research theories on the concept, dimensions, and scales of interpersonal social presence, as well as social presence in online social networking-related studies, to explore the social presence of robots.25 consider social presence a feeling of being with others. This study argues that in human–robot interaction, this feeling is a human mental state and subjective perception that is generated by humans through direct interaction with a robot with human-like behaviour. In interpersonal social presence, previous researchers have argued that social presence contains the following four dimensions: superficial physical presence, the presence of mental states, presence with behavioural involvement and interaction, and conscious perception of presence. Considering the differences between human–robot interactions and interpersonal interactions in terms of the interaction object, interaction mode, and interaction outcome, this study proposes a model of robot social presence, i.e. the level of social presence of a robot can be judged by the perception of the significant of presence, the level of attention allocation, the perception of interactive expression and information understanding, the perception of emotional interdependence, and the perception of interaction behavioural interactions.In their definition, Heeter and his colleaguesIn the model construction, this study merges two dimensions of interpersonal social presence, i.e. the surface physiological presence and the conscious perception of presence, and proposes the presence dimension of robots’ social presence. According to the previous research, the social presence of humans is an inevitable attribute that is inherent in life, while robots’ social presence is created through interactions with humans after the development of technology to achieve deeper anthropomorphism and a higher level of intelligence. It is only when humans realize that the object of the interaction is a human-like social character with social capabilities that social robots are considered to have a social presence on this basis. It is difficult to make a clear delineation between the two dimensions in human–robot interaction, so they are merged into the presence dimension. In addition, three of the dimensions of the final model of robots’ social presence directly refer to the three secondary dimensions of the attention allocation, the perceived message understanding, and the degree of emotional dependence of the mental state social presence dimension in human interaction, i.e. attention allocation, interactive expression and information understanding, and perceived emotional interdependence.60. Attention-related changes have been found to be closely related to the neural effects of social presence in studies measuring changes in social task-related responses. In human–computer interactions, the degree of human attentional change due to the interaction can reflect the significant degree of the human’s perception of robot presence, and changes in attentional allocation are an important dimension reflecting the perception of social presence. Short et al.45 argued that social presence describes the degree of communication and information exchange through which interactants can convey information, express emotions, and establish and develop relationships.Presence is generated by connections, and the presence of an interacting object generates behavioural enhancement support, increased attention, etc., which establish the first connections for interaction in the human brain61. Interactors feel higher levels of mutual knowledge, understanding, and greater empathy, which in turn leads to strong psychological dependence, so that emotional interaction deepens the level of social presence felt by the person, and the degree of emotional interdependence is one of the key factors affecting the level of robot presence.In human–robot interactions, the ability of both interacting objects to transmit information in real time and the mutual perception of the accessibility of the transmission can measure the quality of the interaction. The speed with which the robot receives information and the quality of the feedback affects the human’s judgements about the degree of robot humanity and the effectiveness of the interaction, which in turn affects the human’s judgements about the salience of social presence. Interactors can also perceive each other’s emotional responses through interaction. Emotional displays and interactive responses are key to prompting human–robot interaction, and as human interest in human–robot interaction increases, there is growing interest in the ways in which robot intelligence and emotion can be implemented, with the expectation of enhancing emotional communication and maximizing the social response of the interacting object49 to propose a final model with five dimensions. In the interpersonal social presence measurement model of Nario-Redmond et al.44 and the network social presence model of Biocca28, the emotional expression and understanding is one of the important manifestations of the presence of mental states in the interactor’s level of presence, and the level of interaction deepens from surface physical presence to the presence of mental states through emotional expression and empathic perception. Because the inherent physiological attributes of human interactors in human interactions allow them to achieve physiological emotional expression and relatively accurate psychological emotional understanding, in the human–computer interaction environment, robots mainly express emotions through information such as facial expressions, behavioural gestures, and voice intonation62, with facial expressions being the most direct and effective way to express emotional information63. The degree of anthropomorphism and the level of intelligence of a social robot determine the accuracy of the interactor in recognizing and understanding the robot’s emotions.In this study, an original model of six-dimensional robots’ social presence was proposed, the measurement dimensions were revised based on expert evaluation and user interviews on the characteristics of social robots, and the emotional understanding and expression dimensions were removedExperts believe that the technical level of social robots cannot achieve enough anthropomorphic facial expressions to serve as the most important way for human users to identify emotional expressions, and that the development and design of social robots is limited by the level of human acceptance of humanoid robots, which weakens the processing of facial expressions and other emotional expressions, and that human judgement of robot emotions is vague and the recognition accuracy is low; thus, it cannot accurately reflect the robot’s level of social presence. The results of user interviews show that robots are generally unable to display complex facial expressions, have a single voice tone, lack human-like emotional ups and downs, and display fewer types of emotions in daily interactions. Meanwhile interactors tend to ignore the robot’s emotions in interactions or directly assume that the robot does not have emotions, but the lack of emotions in the design does not affect the effectiveness of the human–robot interactions, suggesting that robot’s emotional expression is not a key factor affecting the robots’ social presence.The scale in this study measured only two basic questions after censoring the measurement questions in the interaction expression and information comprehension dimension, which can be supplemented with refined questions in the subsequent targeted discussion of the perceived degree of comprehension of social robot-subject interactions to facilitate further, targeted measurement of robots’ social presences. At last, in further study, in view of the differences between the way Asian countries treat technology and western cultures, which will lead to the differences in the understanding of the scale by western cultures, the scale will also be tested across cultures.Based on theories and concepts related to human interaction and social presence in a networked environment, this study aims to propose a definition of social robot social presence, construct a model, and propose a corresponding measurement scale. This study defines robots' social presence, i.e. in the case of human individuals interact directly with a social robot with artificial intelligence, if the individual can perceive the same or similar feelings as when interacting with a real human, then the latter is considered to have social presence. In this study, a robot social presence model containing the following five dimensions was constructed: presence, attention allocation, interactive expression and information understanding, perceived emotional interdependence, interaction behaviour perception. The corresponding 17-question measurement questionnaire was obtained, and the scale was validated.In the expert evaluation and user research, the six dimensions of the original model were revised, and the emotional understanding and expression dimensions were deleted based on the current situation analysis of experts regarding the emotional expression of social robots and the human emotion judgement ability and the investigation of the individual’s acceptance of the robot’s emotional expression and functional requirements during the interaction. The revised model dimensions were verified in the following data analysis. The results of the two rounds of questionnaire research showed good reliability of the measurement questionnaire, high inter-factor correlation, high model fit, high internal consistency of the dimensions, high reliability of the scale, and high convergent validity of all dimensions.This study provides a conceptual specification and measurement tool of the social attributes of robots for researchers related to social robots and provides a theoretical basis and measurement scale for further explore the improvement of the quality of human life and the effectiveness of human–robot interactions with social robots. In future research, this study will be further refined and the limitations of the scale will be explained. First, we need a larger range of people of different age levels and education levels to participate in the study. Second, how to measure the effect of emotional dependence on the level of robots’ social presence in human–computer interaction under the existing conditions may require specific experimental sessions for obtaining the measurement. At last, in further research, we will expand the size and audience of the study and expect further extensions to the existing measurement tools.


This may take various forms, from participatory women’s groups and community health committees to public advocacy days. While research suggests a positive influence of CE on MNH outcomes, such as mortality or care-seeking behaviour, there is a need for further evidence on the processes of CE in different settings in order to inform the future development and implementation of CE across programmes. Communication is an integral component of CE serving as a link between the programme and community. The aim of the realist review described in this protocol is to understand Realist review methodology will be used to provide a causal understanding of what communication for CE interventions in MNH programming work, for whom, to what extent, why, and how. This will be done by developing and refining programme theories on communications for CE in MNH through a systematic review of the literature and engaging key experts for input and feedback. By extrapolating context-mechanism-outcome configurations, this review seeks to understand how certain contexts trigger or inhibit specific mechanisms and what outcomes this interaction generates when communication in CE interventions is used in MNH programming.A realist philosophy is well-suited to address the aims of this study because of the complex nature of CE. The review findings will be used to inform a realist evaluation case study of CE for an MNH programme in order to ascertain transferable findings that can inform and guide engagement activities in various settings. Findings will also be shared with stakeholders and experts involved in the consultative processes of the review (through workshops or policy briefs) in order to ensure the relevance of these findings to policy and practice.PROSPERO CRD42022293564The online version contains supplementary material available at 10.1186/s13643-022-02061-9. Improving maternal and newborn health (MNH) has been highlighted as a global priority in both the Millennium Development Goals 2000–2015) and the Sustainable Development Goals 2016–2030). MNH refers to “the health of women during pregnancy, childbirth, and the postpartum period . . . and [in] the babies’ first month of life” [5 and the0. MNH reIn 2015, the World Health Organization (WHO) released recommendations for community-based health promotion interventions to improve MNH . These iCommunity engagement has become an increasingly employed component of programmes across disciplines because it can empower populations and build capacity for longer-term solutions by promoting participation to improve health –20. WhilThe broader outcomes of CE vary with the goals or purpose of the intervention in which it is incorporated . CE has Researchers and practitioners have explored how CE contributes to achieving these outcomes. Studies on women’s groups, for example, have suggested this intervention increases community capacity to identify MNH problems and mobilize resources to address them . PreviouIn addition to these factors, local contexts and histories, social structures and power dynamics, and community and individual beliefs are essential elements to consider in CE programmes , 37, 38.A number of factors have been described above as important considerations in the development or application of CE, yet the literature is limited when it comes to how and why these factors are important and/or influential, especially across different contexts. This may be in part because the research in this field is often focused on reporting health outcomes without a clear understanding of what causes these outcomes . As a reWhile the wide and varied literature on CE describes its associations with specific outcomes  and many interventions claim to incorporate elements of CE, uncertainty remains around what principles and practices make CE “effective” across varied contexts , 29, 44.As described previously, the local contexts and settings in which CE interventions are implemented have been recognized as influential components to the programmes. A realist approach is useful to explicitly understand and document this. Furthermore, the importance of generative causation that underpins the realist philosophy acknowledges that social systems influence individuals and vice versa . This afhow, why, to what extent, and for whom communications in CE contribute to intended and unintended outcomes in MNH programming. This will be done by developing and refining initial programme theories (IPTs) on communications for CE in MNH through reviewing the literature and engaging an expert advisory committee for input and feedback. In order to limit the scope of the review to circumstances specific to this type of programming, this review will focus on MNH interventions and programmes. However, consistent with the realist approach, the scope and specific question for this review have been iteratively refined and further narrowed to specifically consider the communications component of CE [The realist review described in this protocol will aim to understand nt of CE , 55. Whint of CE , revieweRealist reviews are iterative by nature, and the refinement of theory happens throughout the review and informs the review itself. Figure Additionally, the process and the overall steps of the realist review laid out by this protocol are exhibited in Fig. A relevant scoping review is ongoing with the aim of establishing the definitions and descriptions of CE in the literature, which will further inform this realist review . Given tAligned to realist review methodology, a committee of key experts will be consulted throughout the realist review process , 56, 59.A deliberate literature search was conducted to develop candidate IPTs. In order to be useful from a realist perspective, this initial search and candidate IPT development also considers potential contexts, mechanisms, and outcomes of CE. This process has involved reviewing known, fundamental studies and drawing on reflections from the lead researcher’s experiences. For example, the previously mentioned framework for CE developed in Sierra Leone served as a starting point in the consideration and development of candidate IPTs . The delMembers of the expert advisory committee received a draft of five candidate IPTs to review and provide feedback (Additional file Previous literature on realist programme theory suggests using pre-existing theories or frameworks to inform and organize IPT development . Based oFigure The first step in phase 2 involves searching for evidence in the literature based on the elicited candidate IPTs from phase 1. The terms used for this search strategy have been informed by key publications in the relevant fields. Table The bibliographies and reference section of all relevant articles will also be hand searched for potential additional inclusion. CLUSTER searching may also be a useful search technique in this process . This wiThe timeframe is limited to 2000 in order to reflect the increase in attention and literature on CE over the last two decades and to capture the most relevant evidence. While the search does not include a geographic limitation, the relevance of studies based on their setting will be considered in the next step of selecting and appraising evidence. The IPTs described in Fig. Due to the iterative nature of searching for evidence in a realist review, the search for empirical evidence continues to be informed as documents are identified , 75. AddDocuments will be reviewed based on their relevance, richness, and rigour Fig. , in lineA data extraction form has been developed based on the cIPTs and the aims of the review Table . This wiThe CMOCs identified in the included documents will be analysed. This data will be synthesized by reviewing how the CMOCs contribute to the IPTs for theory refinement. Realist approaches use abductive and retroductive reasoning to synthesize data and draw conclusions . AbductiThis review will follow the approach to data analysis and synthesis outlined by Gilmore et al. . Data exIn the process of refining initial programme theories, an additional search may be conducted to provide evidence and literature on wider substantive theory that can aid in explanation and refinement of the theory –55. At tThe purpose of this realist review will be to refine and develop IPTs that explain “how, why, to what extent, and for whom” communication for CE in MNH works within LMICs. By identifying contextual factors, the mechanisms they trigger, and the intended and unintended outcomes of CE, this realist review will provide an understanding of the generative causation of CE in these settings. These findings will address an identified gap in the literature to move beyond the binary outcomes of CE to answer the questions of what is causing these outcomes . A previThe findings of this realist review will have not only theoretical impact but also practical impact by informing the future planning and design of CE communications for MNH interventions. The IPTs refined throughout the review process can be further studied for their potential transferability across settings and programmes and serve as an evaluation starting point for researchers investigating CE in other fields. The ultimate goal of this research is to develop a better understanding of CE in MNH that can be translated to recommendations for policy and practice.both the contexts and mechanisms that lead to intended or unintended outcomes, realist approaches are applicable to study the variations in where and how CE work is conducted [There are several strengths of a realist approach that make it well-suited to consider CE for MNH. CE is a complex process itself, and realist philosophy acknowledges and addresses the dynamic nature of these interventions . Notablyonducted . AdditioThere are a number of limitations to consider and be aware of in executing this realist review. While substantive theory on CE will be incorporated, this review will focus on literature relating to CE in MNH programmes specifically. However, it is recognized that CE used in other types of programmes could provide important insights. A potential challenge of this review will be the large variation in CE literature as well as the range of different definitions and terminology used to refer to engagement versus involvement, participation, mobilization, etc. Reviewers will attempt to account for some of these limitations by documenting decisions and review processes to ensure transparency and potential replicability , 56. FinThis protocol will be used to guide and conduct the described realist review. The results of this review will be written up according to the RAMESES publication standards for realist syntheses and submitted for publication in a peer-reviewed journal . AdditioAdditional file 1. PRISMA-P 2015 ChecklistAdditional file 2. Initial Queries to Expert Advisory CommitteeAdditional file 3. Candidate Initial Programme Theories Shared with Expert Advisory Committee for Feedback


Is the presence of an academic medical center (AMC) within a health care market associated with better outcomes for patients treated at neighboring nonteaching hospitals?In this retrospective cohort study of 22 509 824 hospitalizations among older Medicare beneficiaries at US acute care hospitals from 2015 to 2017, receiving care in a market with high AMC presence was associated with lower mortality and a greater number of healthy days at home among patients treated at non-AMCs.These findings suggest that AMCs may have spillover effects on outcomes for patients treated at neighboring nonteaching hospitals. This cohort study examines whether market-level academic medical center presence is associated with outcomes for Medicare patients treated at nonteaching hospitals within the same markets. Studies suggest that academic medical centers (AMCs) have better outcomes than nonteaching hospitals. However, whether AMCs have spillover benefits for patients treated at neighboring community hospitals is unknown.To examine whether market-level AMC presence is associated with outcomes for patients treated at nonteaching hospitals within the same markets.This retrospective, population-based cohort study assessed traditional Medicare beneficiaries aged 65 years and older discharged from US acute care hospitals between 2015 and 2017 (100% sample). Data were analyzed from August 2021 to December 2022.The primary exposure was market-level AMC presence. Health care markets  were categorized by AMC presence  as follows: no presence (0%), low presence (>0% to 20%), moderate presence (>20% to 35%), and high presence (>35%).The primary outcomes were 30-day and 90-day mortality and healthy days at home (HDAH), a composite outcome reflecting mortality and time spent in facility-based health care settings.P < .001) and 90-day mortality  and more HDAH at 30 days  and 90 days , after adjustment. There was no association between market-level AMC presence and mortality for patients treated at AMCs themselves.There were 22 509 824 total hospitalizations, with 18 865 229 (83.8%) at non-AMCs. The median (IQR) age of patients was 78 (71-85) years, and 12 568 230 hospitalizations (55.8%) were among women. Of 306 hospital referral regions, 191 (62.4%) had no AMCs, 61 (19.9%) had 1 AMC, and 55 (17.6%) had 2 or more AMCs. Markets characteristics differed significantly by category of AMC presence, including mean population, median income, proportion of White residents, and physicians per population. Compared with markets with no AMC presence, receiving care at a non-AMC in a market with greater AMC presence was associated with lower 30-day mortality (9.5% vs 10.1%; absolute difference, −0.7%; 95% CI, −1.0% to −0.4%; AMCs may have spillover effects on outcomes for patients treated at non-AMCs, suggesting that they have a broader impact than is traditionally recognized. These associations are greatest in markets with the highest AMC presence and persist to 90 days. Multiple studies21 have demonstrated better outcomes, on average, for patients treated at academic medical centers (AMCs) compared with those treated at nonteaching hospitals.Variation in quality among clinicians and hospitals in the US is well documented, with meaningful differences in serious outcomes such as mortality.17 it is not feasible for most patients to receive treatment at an AMC. However, it is possible that AMCs provide local benefits beyond direct patient care. Formal and informal arrangements between AMCs and community hospitals may include sharing clinical staff, disseminating protocols, and serving as a source of newly trained physicians. In addition, patients in communities with greater AMC presence may have better access to clinical trials or other advances. Yet, evidence on possible spillover benefits of AMCs is lacking. A fuller understanding of the broader impact of AMCs is especially important given the substantial federal spending on graduate medical education.23However, given that AMCs account for a minority of US hospitals (approximately 6%),We sought to address this evidence gap using national Medicare claims to investigate the following questions: First, how does AMC presence vary among health care markets in the US? Second, do patients hospitalized at non-AMCs have lower mortality when a health care market has greater AMC presence? Third, do patients hospitalized at non-AMCs have a greater number of healthy days at home (HDAH), a population-based outcome measure that combines mortality and total time spent out of facility-based health care settings, when hospitalized in a market with greater AMC presence?STROBE) reporting guideline for observational studies.The Office of Human Research Administration at the Harvard T.H. Chan School of Public Health approved this cohort study. Informed consent could not be practically obtained for this retrospective study. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology . We classified principal discharge diagnosis into Healthcare Utilization Project Clinical Classification Software categories. We identified beneficiary characteristics  and whether the hospitalization ended in interhospital transfer. Given the known racial disparities in health care access and quality, beneficiary race was identified and reported as defined in Medicare data, which is primarily derived from Social Security Administration data.We defined health care markets by the Dartmouth Atlas hospital referral regions (HRRs), which represent regional markets for tertiary medical care. Using the Dartmouth Atlas, the Centers for Medicare & Medicaid Services Public Use files, and the American Community Survey (2011-2015), we identified the following market characteristics: total population, median income, percentage of residents with income below the poverty line, percentages of Black residents and White residents, number of physicians per 100 000 population, number of primary care physicians per 100 000 population, and number of hospital full-time equivalents (FTEs) per 1000 population.25We identified acute care hospitals in all 50 states and the District of Columbia from the American Hospital Association Annual Survey. We defined hospital teaching status by intern or resident-to-bed ratio (IRB) from the Medicare cost reports. We classified AMCs as those with an IRB of 0.25 or greater and non-AMCs as those with an IRB less than 0.25.29 To calculate 30-day HDAH, we subtracted from 30 the number of days in the follow-up period in which the beneficiary was deceased or in the following settings: inpatient, observation, skilled nursing facility, outpatient emergency department, inpatient psychiatry, inpatient rehabilitation, and long-term hospital.Our primary outcome was mortality (from the Master Beneficiary Summary File) within 30 days of an inpatient stay. To determine whether any associations persisted, we also identified 90-day mortality. In addition, we examined HDAH, an outcome developed by the Medicare Payment Advisory Commission described in prior studies.Data were analyzed from August 2021 to December 2022. For each HRR, we calculated the number of AMCs in 2015, as well as the proportion of hospitalizations managed at AMCs in 2015 to 2017. We created the 4 market categories: no AMC presence , low (>0% to 20%), moderate (>20% to 35%),and high (>35%) AMC presence.One concern about comparing these groups is that they have demographic and health system differences beyond AMC presence that are associated with differential health outcomes. We, therefore, examined how key characteristics varied among the 4 market groups and tested for differences using analysis of variance for means and Kruskal-Wallis for medians.31 our subsequent model further incorporated the diagnosis-related group weight as an indicator of the complexity of the acute condition as well as patient demographics  and chronic conditions. To account for nonrandom distribution of AMCs and hypothesized differences in the types of communities where AMCs are located, our main model further incorporated market characteristics . We calculated mean adjusted rates of 30-day mortality for the 4 market groups and repeated these models for the outcome of 90-day mortality. We specified analogous sets of linear regression models with the outcomes of 30-day and 90-day HDAH. SAS code for the main models is presented in eTable 1 in After calculating raw mortality rates, we constructed a set of linear probability models with 30-day mortality as the outcome and market-level AMC presence as the categorical variable. Our initial model adjusted for year, principal discharge diagnosis, geographic region , and hospital random effects. Given that patient characteristics vary widely and are associated with patient outcomes,In secondary analyses, to evaluate the association for a nonlinear trend, we created a locally estimated scatterplot smoothing curve, plotting the proportion of hospitalizations in an HRR managed at an AMC on the x-axis and unadjusted 30-day mortality on the y-axis. We repeated our analysis for adjusted 30-day mortality using market AMC presence as a continuous variable, adjusting for patient and market characteristics.We considered the possibility that any association between market-level AMC presence and patient outcomes at non-AMCs could be associated with unobserved between-market differences in patient severity, rather than spillover effects of AMCs. If that were the case, we might expect to see similar patterns of outcomes for patients treated at AMCs themselves within these same market categories. Thus, we repeated our models for mortality among patients hospitalized at AMCs, with AMC presence as the categorical variable and adjusting for the same set of covariates.32 It is, thus, possible that equivalently ill patients in markets with low AMC presence may have worse outcomes if they lack access to tertiary care transfer. We, therefore, repeated our models for 30-day and 90-day mortality, incorporating market-level transfer rate. We conducted analyses using SAS statistical software version 9.4 (SAS Institute), using a significance level of P < .05 and calculating 2-sided 95% CIs.One potential mechanism for the association between AMC presence and mortality for patients at non-AMCs could be the differential ability to transfer seriously ill patients. Timely transfer is associated with better outcomes for key conditions.Our sample include 22 509 824 total hospitalizations from 2015 to 2017, of which 18 865 229 (83.8%) were at non-AMCs. The median (IQR) age of patients was 78 (71-85) years, and 12 650 521 hospitalizations (56.2%) were among women. Hospitalizations missing an HRR designation  were excluded from the analysis.The mean number of AMCs per HRR was 0.93 . Of the 306 HRRs in the US, 191 (62.4%) had no AMCs in 2015. Sixty-one (19.9%) had 1 AMC, 24 (7.8%) had 2 AMCs, 12 (3.9%) had 3 AMCs, and 18 (5.9%) had 4 or more AMCs , moderate , and low  AMC presence. These differences persisted after adjusting for patient characteristics (eTable 3 in P < .001). Treatment in a market with low AMC presence was associated with a −0.21% absolute difference in mortality at 30 days compared with a market with no AMCs . There was no significant difference between markets with a moderate AMC presence vs those with no AMCs . Adjusted means by group are shown in Coefficients for all variables in the models adjusting for patient and hospital characteristics are presented in eTable 2 in P < .001) but there was no association for low or moderate AMC presence. Adjusted means by model are shown in eFigure 4 in Our results were similar for 90-day mortality. Compared with markets with no AMC presence, treatment at a non-AMC in markets with low, moderate, and high AMC presence with associated lower 90-day mortality in the unadjusted and patient characteristics models (eTable 3 in P = .005) . For 90-day HDAH, treatment in a market with high AMC presence was associated with 1.25 more HDAH compared with markets with no AMC presence , and treatment in a market with low AMC presence was associated with 0.41 additional HDAH  in the final model incorporating market characteristics. However, there was no association for markets with moderate AMC presence .When examining AMC presence as a continuous variable, the locally estimated scatterplot smoothing curve revealed a small decline in 30-day mortality with increasing AMC presence   (eTable 3 and eFigure 4 in As noted in In a national cohort study of more than 22 million hospitalizations among traditional Medicare beneficiaries from 2015 to 2017, patients hospitalized at non-AMCs had lower 30-day and 90-day mortality and more HDAH at 30 and 90 days when they received care in markets with greater AMC presence. Adjusting for patient characteristics increased this association relative to the unadjusted model, which persisted even when accounting for other demographic and health system factors that differed among the market groups. In contrast, we found no association between market-level AMC presence and outcomes for patients treated at AMCs themselves. Taken together, these results suggest a spillover effect of AMCs on outcomes for neighboring community hospitals and that the benefit of AMCs for the broader community may be greater than is traditionally recognized.33 Prior research has suggested that transfer is associated with better outcomes for selected conditions32 and that the failure to transfer in a timely fashion represents a source of inequity.35 When we accounted for market-level differences in transfer rates, the association between high AMC presence and lower mortality for patients hospitalized at non-AMCs was smaller but remained significant. These findings suggest that variation in tertiary care transfer likely accounts for some, but not all, of the possible mortality benefit of receiving care in a market with greater AMC presence for patients hospitalized at non-AMCs.There are multiple potential mechanisms by which AMCs may improve outcomes for patients in community hospitals. Patients hospitalized at non-AMCs in regions with higher AMC presence were more often transferred to another short-term general hospital for ongoing care. It is possible that greater AMC availability in these markets allowed non-AMCs to transfer patients who would most benefit from advanced capabilities at tertiary centers.36 with more than one-half of residency graduates historically practicing in the same state after graduation.38 Thus, it is possible that the presence of an AMC may lead to a more robust physician supply and better patient outcomes at community hospitals. Alternatively, it may be that a number of formal and informal affiliations between AMCs and non-AMCs within the same market leads to diffusion of knowledge, innovation, and even sharing of clinicians who work at multiple sites.40 For key serious conditions, such as a stroke, AMCs have been shown to promote evidence-based practice and improve patient outcomes at community hospitals.41Markets with the greatest AMC presence had more nurses and physicians per capita. There is evidence that physicians tend to practice in close geographic proximity to where they trained,21 have suggested that patients treated at AMCs have better outcomes across the spectrum of patient severity. To our knowledge, this is the first study to systematically examine the potential indirect clinical benefits of receiving care at a nonteaching hospital with greater proximity to 1 or more AMCs across a wide range of conditions.This study extends prior work examining the association of AMCs with acute care outcomes. Multiple studies42 particularly for key specialty services.43 These data highlight the degree to which AMCs tend to be concentrated, with rural regions having less access to AMC services on average. Thus, identifying strategies by which AMCs may enhance care for patients in rural and other remote locations46 has the potential to improve health outcomes for underserved populations and widen the reach of the nation’s academic health care institutions.This study is consistent with other studies demonstrating geographic disparities in health care access,The observational nature of this study precludes assigning a causal relationship between market-level AMC presence and outcomes. Markets with differing degrees of AMC presence may differ on other key characteristics of interest. We addressed these concerns by specifying models incorporating observable indicators of patient severity and characteristics of the markets themselves. However, it is possible that there are residual patient and market confounders that may impact our results. To address this, we repeated our models among patients hospitalized at AMCs themselves, hypothesizing that if market factors were the key factors associated with any differential patient outcomes—rather than spillover effects of AMCs within the market—then we would see analogous trends for patients hospitalized at AMCs. In addition, although this study examines all hospitalizations among traditional Medicare beneficiaries, it is possible that the results may differ for other populations. Furthermore, we chose a linear probability model because the results can be interpreted as absolute differences in percentages between category of HRR-level AMC presence. Absolute differences are more interpretable than relative differences reflected in odds ratios from a logistic regression model. However, the linear probability model has technical disadvantages because the model can estimate probabilities below 0 or above 1 and does not allow for unequal variances.Among Medicare beneficiaries hospitalized at non-AMCs from 2015 to 2017, this cohort study found that treatment in a market with greater AMC presence was associated with lower mortality and a greater number of HDAH at 30 and 90 days. This association persisted after accounting for differences in patient and market characteristics, including variation in rates of interhospital transfer. These findings suggest that the impact of AMCs on clinical outcomes extends beyond direct patient care and that there are spillover effects of AMCs on outcomes for patients in the broader health care market.