Relevant Domains Research Articles

Intersexual and Intrasexual Differences in Mate Selection Preferences Among Lesbian Women, Gay Men, and Bisexual Women and Men

Sex differences in mating strategies and partner preferences are well established. However, most research solely focused on heterosexual women and men. We examined the mate selection, marriage, and age preferences of a sample of lesbian women, gay men, and bisexual women and men (LGB) who took part in an online dating survey. Additionally, we analyzed inter- and intrasexual differences in these preferences. A total of 710 participants rated the importance of 82 mate selection criteria and 10 marriage criteria, and they also indicated their age preferences and short-term and long-term relationship orientation. An exploratory factor analysis suggested 11 relevant domains of mate selection in the LGB sample, with sex, age, and long-term relationship orientation being relevant predictors of differences in these domains. We compared the LGB data with data collected from 21,245 heterosexual women and men on the same mate selection criteria. Results showed that the participants’ sex was the most important predictor of differences in mate selection and marriage preferences, while intrasexual variables (sexual orientation and relationship orientation) explained only a small part of the variance. We incorporated the results into the current discussion about partner preferences and sexual orientation.

SLC12A Cryo-EM: Analysis of relevant ion binding sites, structural domains and amino acids.

The SLC12A superfamily of membrane transporters modulate the movement of cations coupled with chloride across the membrane. In doing so, these cotransporters are involved in numerous aspects of human physiology: cell volume regulation, ion homeostasis, blood pressure regulation, and neurological action potential via intracellular chloride concentration modulation. Their physiological characterization has been largely studied; however, understanding the mechanics of their function and relevance of structural domains or specific amino acids has been a pending task. In recent years, single-particle cryogenic electron microscopy (cryo-EM), has been successfully applied to members of the SLC12A family including all KCCs, NKCC1 and recently NCC; revealing structural elements that play key roles in their function. The present review analyzes the data provided by these cryo-EM reports focusing on structural domains and specific amino acids involved in ion binding, domain interactions, and other important SCL12A structural elements. Comparison of cryo-EM data from NKCC1 and KCCs is presented in the light of the two recent NCC cryo-EM studies, to propose insight into structural elements that might also be found in NCC and are necessary for its proper function. On the final sections, the importance of key coordination residues for substrate specificity and their implication on various pathophysiological conditions and genetic disorders is reviewed, as this could provide the basis to correlate structural elements with the development of novel and selective treatments, as well as mechanistic insight into the function and regulation of CCCs.

A serious web game for children with attentive disorders: design and experiences from two trials

ABSTRACT Cognitive developmental disorders are common in children and can affect various abilities. Attention Deficit/Hyperactivity Disorder (ADHD) is the most prevalent childhood psychiatric condition. This work presents PlayToPickUp, a serious game that aims to stimulate children in relevant cognitive domains (attention and error monitoring). A multidisciplinary team of experts and caregivers from two different centers that support therapeutic activities with such children participated from the games’s inception to the design and the evaluation of the game. Depending on the characteristics and abilities of the player, therapists can customize the game to provide training that best fits the skills and the needs of the child while maintaining the player’s motivation. After its development, the game was used over 2 months “in the wild” by children recruited by the two centers. In one case the children played with it within the regular activities offered by the training center. In the other one, the parents of the children were instructed by caregivers to have the children play the game at home. In the paper, we describe the experience gathered from such two studies run in parallel, discussing the aspects that worked better, those that represented difficulties, and the lesson learnt for future studies.

OPERA: perception of information in patients with gastroenteropancreatic neuroendocrine tumors on lanreotide autogel.

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) can affect patient health-related quality of life (HRQoL). Appropriate information may improve their adherence to treatment and quality of life. To evaluate the change in patient's perceptions of the level of information at lanreotide (LAN) treatment initiation for GEP-NETs vs after 6 months. OPERA (NCT03562091) was a prospective, longitudinal, noninterventional study. Thirty-one centers in France specialized in the management of patients with NETs. Planned clinical visits at enrollment and end-of-study visits at month 6, with completion of the European Organisation for Research and Treatment of Cancer 25-item Quality of Life Questionnaire-Information Module (QLQ-INFO25) and 30-item Quality of Life Questionnaire-Core. Absolute change in the patient's perception of the information between baseline and month 6, using the relevant domains of the QLQ-INFO25. Endpoints measured at baseline and month 6 for at least 1 of the 3 targeted QLQ-INFO25 dimensions of the primary endpoint. Ninety-three of the 115 patients enrolled completed ≥1 primary endpoint information dimension. Mean (SD) scores for the primary endpoint information dimensions were high at baseline (disease, 63.41 [20.71]; treatment, 58.85 [19.00]; supportive care, 26.53 [24.69]; maximum 100). There were no significant changes between baseline (98.34% CI) and 6 months (disease, -2.84 [-8.69, 3.01; P = .24]; treatment, -4.37 [-11.26, 2.52; P = .13]; supportive care, 0.46 [-6.78, 7.70; P = .88]), and in HRQoL between baseline and 6 months. The lack of change in patient's perceptions of the disease, treatment, and supportive care information provided over the first 6 months of LAN treatment may suggest that physicians provided adequate information at the treatment initiation.

Boundary bypass activity in the abdominal-B region of the Drosophila bithorax complex is position dependent and regulated.

Expression of Abdominal-B (Abd-B) in abdominal segments A5-A8 is controlled by four regulatory domains, iab-5-iab-8. Each domain has an initiator element (which sets the activity state), elements that maintain this state and tissue-specific enhancers. To ensure their functional autonomy, each domain is bracketed by boundary elements (Mcp, Fab-7, Fab-7 and Fab-8). In addition to blocking crosstalk between adjacent regulatory domains, the Fab boundaries must also have bypass activity so the relevant regulatory domains can 'jump over' intervening boundaries and activate the Abd-B promoter. In the studies reported here we have investigated the parameters governing bypass activity. We find that the bypass elements in the Fab-7 and Fab-8 boundaries must be located in the regulatory domain that is responsible for driving Abd-B expression. We suggest that bypass activity may also be subject to regulation.

Identifying domains of health-related quality of life: the perspective of individuals with low back pain

BackgroundIdentifying the most relevant HRQOL domains for LBP from the perspective of individuals with lived experience with LBP is necessary to prioritize domains that will be most informative for evaluating the impact of pain and interventions while overcoming the burden of using long-form assessment tools. This study aimed to identify which domains of HRQOL are most important from the perspective of individuals with chronic LBP.MethodsSemi-structured interviews were conducted with 26 individuals with LBP. Participants first responded to questions related to the impact of their LBP on their HRQOL. Then, using a card sorting method, they were asked to select and indicate HRQOL domains that were most relevant to them from a list of 18 cards that represented different HRQOL domains. Participants were asked to explain the reasoning for their selection.ResultsParticipants identified physical activity restriction (50%), severity of pain (31%), social activity restriction (23%), and work performance restriction (23%) as the most important domains. The most frequently selected HRQOL domains during card sorting were social function (69%), pain intensity (62%), physical function (58%), fatigue (58%), and pain interference (42%).ConclusionThe most important domains of HRQOL perceived by participants were pain intensity, social function, physical function, fatigue, and pain interference. Identifying these domains will inform clinical decision-making and guide treatment choices for health care providers.

A Novel Integrative Design Framework Combining 4D Sketching, Geometry Reconstruction, Micromechanics Material Modelling, and Structural Analysis

State-of-the-art workflows within Architecture, Engineering, and Construction (AEC) are still caught in sequential planning processes. Digital design tools in this domain often lack proper communication between different stages of design and relevant domain knowledge. Furthermore, decisions made in the early stages of design, where sketching is used to initiate, develop, and communicate ideas, heavily impact later stages, resulting in the need for rapid feedback to the architectural designer so they can proceed with adequate knowledge about design implications. Accordingly, this paper presents research on a novel integrative design framework based on a recently developed 4D sketching interface, targeted for architectural design as a form-finding tool coupled with three modules: (1) a Geometric Modelling module, which utilises Points2Surf as a machine learning model for automatic surface mesh reconstruction from the point clouds produced by sketches, (2)a Material Modelling module, which predicts the mechanical properties of biocomposites based on multiscale micromechanics homogenisation techniques, and (3) a Structural Analysis module, which assesses the mechanical performance of the meshed structure on the basis of the predicted material properties using finite element simulations. The proposed framework is a step towards using material-informed design already in the early stages of design.

Caution! AI Bot Has Entered the Patient Chat: ChatGPT Has Limitations in Providing Accurate Urologic Healthcare Advice

To conduct the first study examining the accuracy of ChatGPT, an artificial intelligence (AI) chatbot, derived patient counseling responses based on clinical care guidelines in urology using a validated questionnaire. We asked ChatGPT a set of 13 urological guideline-based questions three times. Answers were evaluated for appropriateness and using Brief DISCERN (BD), a validated healthcare information assessment questionnaire. Data analysis included descriptive statistics and Student's t test (SAS Studio). 60% (115/195) of ChatGPT responses were deemed appropriate. Variability existed between responses to the same prompt, with 25% of the 13 question sets having discordant appropriateness designations. The average BD score was 16.8±3.59. Only 7 (54%) of 13 topics and 21 (54%) of 39 responses met the BD cut-off score of ≥16 to denote good-quality content. Appropriateness was associated with higher overall and Relevance domain scores (both P<.01). The lowest BD domain scores were for Source categories, since ChatGPT does not provide references by default. With prompting, 92.3% had ≥1 incorrect, misinterpreted, or nonfunctional citations. While ChatGPT provides appropriate responses to urological questions more than half of the time, it misinterprets clinical care guidelines, dismisses important contextual information, conceals its sources, and provides inappropriate references. Chatbot models hold great promise, but users should be cautious when interpreting healthcare-related advice from existing AI models. Additional training and modifications are needed before these AI models will be ready for reliable use by patients and providers.

COMPARING THE EFFICACY OF EARLY INTERVENTION AND LATE INTERVENTION FOR AUTISM SPECTRUM DISORDER IN CHILDREN AND ADULTS: A SYSTEMATIC REVIEW AND META-ANALYSIS

Objective: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by challenges in social interaction, communication, and repetitive behaviors. Early intervention is widely recognized as crucial for improving outcomes in individuals with ASD. However, there is ongoing debate regarding the optimal timing of intervention. This systematic review and meta-analysis aimed to compare the efficacy of early intervention and late intervention for individuals with ASD. The study aimed to synthesize existing evidence and provide a comprehensive analysis of the effectiveness of these two intervention approaches in improving outcomes for children and adults with ASD.&#x0D; Methods: A comprehensive search of electronic databases was conducted to identify relevant studies. The search terms included keywords related to ASD, early intervention, late intervention, children, adults, and outcome measures. Studies were selected based on predefined inclusion and exclusion criteria, focusing on randomized controlled trials and comparative studies with adequate sample sizes. The selected studies underwent rigorous quality assessment and data extraction. Meta-analysis was conducted using statistical software to determine the overall efficacy of early intervention and late intervention in improving outcomes related to social interaction, communication, adaptive functioning, and other relevant domains.&#x0D; Results: The distribution of cases according to expressive language and motor skills was analyzed in two groups: Group A (early intervention) and Group B (late intervention). The results showed no statistically significant differences in the distribution of cases according to expressive language and motor skills between the two groups. The findings suggested that the timing of intervention may not strongly influence the distribution of these skills in individuals with ASD.&#x0D; Conclusion: This study found no significant differences in the distribution of cases according to expressive language and motor skills between early and late intervention groups. These findings are consistent with previous research, indicating that the distribution of these skills may not be strongly influenced by the timing of intervention. However, further research with larger samples and standardized assessment methods is needed to gain a comprehensive understanding of the relationships between intervention timing and the distribution of these skills in individuals with ASD.

Choice of conceptualization of a problem situation by an intelligent agent in decision-making tasks

The research in the domain of autonomous intelligent agent is the foreground of the introduction of artificial intelligence solution in all areas of economy. The intelligent autonomous systems combine the usage of pattern recognition, reasoning, decision making, conceptual modeling techniques and methods. The important part of intelligent agent implementation is to find the conceptualization which is suitable to the current problematic situation. Despite all progress around autonomous intelligent agents, humans are much more flexible and creative in making the right conceptualizations. They seamlessly adapt to the situation at hand and filter out all irrelevant details, using multiple perspectives and representations for the same objects. This research makes assumptions that every intelligent agent dynamically creates its own ontology used to interpret local knowledge. The mappings are established with this local ontology and the ontologies of other agents when needed, in order to share and reuse knowledge. In the article a formal model of problematic situation in the context of decision-making operation is presented. Models used in decision making and their relationships are described. In the second part of the article we analyze the process of conceptualization selection and arrive to the conclusion that this selection is done on multiple levels, starting from selecting the communicating agent with relevant domain of expertise, selecting and aligning ontologies of agents, selecting patterns and patterns languages which better correspond to the situation and lastly, selecting the relevant interpretations of concepts and relationships. In the last part of article, the problem of the selection of relevant knowledge provider is solved, using modified TOPSIS method. The proposed approach and directions of research will help to add flexibility to conceptual modeling of problematic situations by intelligent agents.

HKGCL: Hierarchical graph contrastive learning for multi-domain recommendation over knowledge graph

Multi-domain recommendation (MDR) aims to improve the recommendation performance in all target domains simultaneously by leveraging rich data from relevant domains. However, conventional approaches either only rely on sparse interactions in each separate scenario, or simply combine multiple shared recommendation solutions, resulting in MDR systems still suffering from severely low recommendation accuracy. To tackle the above issues, we propose a novel recommendation model called Hierarchical Graph Contrastive Learning for Multi-Domain Recommendation over Knowledge Graph (HKGCL). Different from previous MDR models, our HKGCL can treat each relevant domain as a hierarchy in the interaction network. Based on the LightGCN aggregation strategy, a hierarchical message passing mechanism over the knowledge graph is designed to aggregate hierarchical knowledge representations for users and items. Then, a hierarchical node dropping scheme is proposed as our data augmentation technique on different hierarchical views to obtain more self-supervised semantic signals. Subsequently, three graph contrastive learning tasks in multiple and separate domains are proposed to explore domain-shared and domain-specific preference features for target users, respectively. Additionally, a novel Multi-domain Bayesian Personalized Ranking (MBPR) approach is proposed to assist in training our multi-task learning framework. Extensive experiments conducted on two real-world datasets demonstrate the consistent superiority of our proposed HKGCL over various state-of-the-art baselines, and also verify that the HKGCL can achieve strong performance on sparse interaction scenarios.

Barriers to Implementing the Kidney Disease Outcomes Quality Initiative End-Stage Kidney Disease Life Plan Guideline

ObjectiveThe updated 2019 National Kidney Foundation Kidney Disease Outcomes Quality Initiative vascular access guidelines recommend patient-centered, multi-disciplinary construction and regular update of an individualized end-stage kidney disease (ESKD) Life-Plan (LP) for each patient, a dramatic shift from previous recommendations and policy. The objective of this study was to examine barriers and facilitators to implementing the LP among key stakeholders.MethodsSemi-structured individual interviews were analyzed using inductive and deductive coding. Codes were mapped to relevant domains in the Consolidated Framework for Implementation Research (CFIR).ResultsWe interviewed 34 participants: 11 patients with end-stage kidney disease, 2 care partners, and 21 clinicians who care for patients with end-stage kidney disease. In both the clinician and the patient/care partner categories, saturation (where no new themes were identified) was reached at 8 participants. We identified significant barriers and facilitators to implementation of the ESKD LP across three CFIR domains: Innovation, Outer setting, and Inner setting. Regarding the Innovation domain, patients and care partners valued the concept of shared decision-making with their care team (CFIR construct: innovation design). However, both clinicians and patients had significant concerns about the complexity of decision-making around kidney substitutes and the ability of patients to digest the overwhelming amount of information needed to effectively participate in creating the LP (innovation complexity). Clinicians expressed concerns regarding the lack of existing evidence base which limits their ability to effectively counsel patients (innovation evidence base) and the implementation costs (innovation cost). Within the Outer Setting, both clinicians and patients were concerned about performance measurement pressure under the existing “Fistula First” policies and had concerns about reimbursement (financing). In the Inner Setting, clinicians and patients stressed the lack of available resources and access to knowledge and information.ConclusionGiven the complexity of decision-making around kidney substitutes and vascular access, our findings point to the need for implementation strategies, infrastructure development, and policy change to facilitate ESKD LP development.

The Patient Experience of Gout Remission: A Qualitative Study.

Preliminary remission criteria for gout have been developed. However, the patient experience of gout remission has not been described. This qualitative study aimed to understand the patient experience of gout remission and views about the preliminary gout remission criteria. Semistructured interviews were conducted. All participants had gout, had not had a gout flare in the preceding 6 months, and were on urate-lowering medication. Participants were asked to discuss their experience of gout remission and views about the preliminary remission criteria. Interviews were audio recorded and transcribed verbatim. Data were analyzed using a reflexive thematic approach. Twenty participants with gout (17 male participants, median age 63 years) were interviewed. Four key themes of the patient experience of remission were identified: 1) minimal or no gout symptoms (absence of pain due to gout flares, good physical function, smaller or no tophi), 2) freedom from dietary restrictions, 3) gout is "not on the mind", and 4) multifaceted management strategies to maintain remission (regular urate-lowering therapy, exercise, healthy eating). Participants believed that the preliminary remission criteria contained all relevant domains but considered that the pain and patient global assessment domains overlapped with the gout flares domain. Participants regarded 12 months as a more suitable time frame than 6 months to measure remission. Patients experience gout remission as a return to normality with minimal or no gout symptoms, dietary freedom, and absence of mental load. Patients use a range of management strategies to maintain gout remission.

Frameworks in Historiography: Explanation, Scenarios, and Futures

Abstract In this paper, I analyze how frameworks shape historiographical explanations. I argue that, in order to identify a sequence of events as relevant to a historical outcome, assumptions about the workings of the relevant domain have to be made. By extending Lakatosian considerations, I argue that these assumptions are provided by a framework that contains a set of factors and intertwined principles that (supposedly) govern how a historical phenomenon works. I connect frameworks with a counterfactual account of historical explanation. Frameworks enable us to explain the past by providing a backbone of explanatory patterns of counterfactual dependency. I conclude by noting that both counterfactual scenarios and scenarios of the future require frameworks and, therefore, historiographical explanation generates a set of possible futures. Analyzing these possible futures enables us to reveal the theoretical commitments of historiography.

The eliminate hepatitis C (EC) experience study: baseline characteristics of a cohort of people who inject drugs in Melbourne, Australia

ObjectivesDirect-acting antivirals provide an opportunity to eliminate hepatitis C virus (HCV) as a public health threat in Australia, yet barriers to care remain. In this study, we use baseline data...

Semantically preserving adversarial unsupervised domain adaptation network for improving disease recognition from chest x-rays.

Supervised deep learning methods have been successfully applied in medical imaging. However, training deep learning systems often requires ample annotated data. Due to cost and time restrictions, not all collected medical images, e.g., chest x-rays (CXRs), can be labeled in practice. To classify these unlabeled images, a solution may involve adopting a model trained with sufficient labeled data in relevant domains (with both source and target being CXRs). However, domain shift may cause the trained model not able to generalize well on unlabeled target datasets. This work aims to develop a novel unsupervised domain adaptation (UDA) framework to improve recognition performance on unlabeled target data. We present a semantically preserving adversarial UDA network, i.e., SPA-UDA net, with the potential to bridge the domain gap, by reconstructing the images in the target domain via an adversarial encode-and-reconstruct translation architecture. To preserve the class-specific semantic information (i.e., with or without disease) of the original images when translating, a semantically consistent framework is embedded. This framework is designed to guarantee that fine-grained disease-related information on the original images can be safely transferred. Furthermore, the proposed SPA-UDA net does not require paired images from source and target domains when training, which reduces the cost of arranging data significantly and is ideal for UDA. We evaluate the proposed SPA-UDA net on two public CXR datasets for lung disease recognition. The experimental results show that the proposed framework achieves significant performance improvements compared to other state-of-the-art UDA methods.

Scalable Spatiotemporal Graph Neural Networks

Neural forecasting of spatiotemporal time series drives both research and industrial innovation in several relevant application domains. Graph neural networks (GNNs) are often the core component of the forecasting architecture. However, in most spatiotemporal GNNs, the computational complexity scales up to a quadratic factor with the length of the sequence times the number of links in the graph, hence hindering the application of these models to large graphs and long temporal sequences. While methods to improve scalability have been proposed in the context of static graphs, few research efforts have been devoted to the spatiotemporal case. To fill this gap, we propose a scalable architecture that exploits an efficient encoding of both temporal and spatial dynamics. In particular, we use a randomized recurrent neural network to embed the history of the input time series into high-dimensional state representations encompassing multi-scale temporal dynamics. Such representations are then propagated along the spatial dimension using different powers of the graph adjacency matrix to generate node embeddings characterized by a rich pool of spatiotemporal features. The resulting node embeddings can be efficiently pre-computed in an unsupervised manner, before being fed to a feed-forward decoder that learns to map the multi-scale spatiotemporal representations to predictions. The training procedure can then be parallelized node-wise by sampling the node embeddings without breaking any dependency, thus enabling scalability to large networks. Empirical results on relevant datasets show that our approach achieves results competitive with the state of the art, while dramatically reducing the computational burden.

Non-IID Transfer Learning on Graphs

Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorithms focus on IID tasks, where the source/target samples are assumed to be independent and identically distributed. Very little effort is devoted to theoretically studying the knowledge transferability on non-IID tasks, e.g., cross-network mining. To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph. The crucial idea is to characterize the cross-network knowledge transferability from the perspective of the Weisfeiler-Lehman graph isomorphism test. To this end, we propose a novel Graph Subtree Discrepancy to measure the graph distribution shift between source and target graphs. Then the generalization error bounds on cross-network transfer learning, including both cross-network node classification and link prediction tasks, can be derived in terms of the source knowledge and the Graph Subtree Discrepancy across domains. This thereby motivates us to propose a generic graph adaptive network (GRADE) to minimize the distribution shift between source and target graphs for cross-network transfer learning. Experimental results verify the effectiveness and efficiency of our GRADE framework on both cross-network node classification and cross-domain recommendation tasks.

PA10 You and Your Skin: developing and trialling a new history-taking aid for psychological wellbeing in children and young people with skin conditions

Abstract The mental health burden in children and young people (CYP) living with skin conditions is profound and well documented. There is a lack of integrated psychology/psychiatry provision within paediatric dermatology departments, and dermatologists can struggle to explore psychological wellbeing as part of their clinic consultations due to limited time and/or a lack of confidence and/or training. The British Society for Paediatric and Adolescent Dermatology formed a working group to address the mental health needs of CYP presenting with skin conditions. A multidisciplinary consensus group was formed to assess evidence and develop appropriate guidance. A systematic review found that no single psychological measure or patient-related outcome measure (PROM) adequately captures the psychological burden and impact of living with a skin condition. Patient feedback was clear that PROMs can be less useful than an appropriate clinical consultation. This identified a need for an appropriate clinical tool to help explore CYP psychological wellbeing as part of dermatology consultations: You and Your Skin (YAYS) was developed as a self-completed history-taking aid for CYP &amp;gt; 8 years old and a parent/carer for those &amp;lt; 8 years old. The YAYS was created iteratively using adapted validated and clinically relevant domains from the Children’s Dermatology Quality of Life Questionnaire, the Teenage Quality of Life Questionnaire, the British Association of Dermatologists Acne 3 questions and the Patient Health Questionnaire-9, including self-harm and risk. Questions explore how CYP feel about their skin today and how their skin condition has affected their daily life, caused embarrassment and bullying, and impacted on sleep and mood, and whether they had any thoughts of life not being worth living. The YAYS was co-developed with patients, parents/carers and an external expert stakeholder consensus group (dermatologists, psychologists, psychiatrists, general practitioners, nurse specialists and patient support groups) with revisions done according to feedback on the usability, accessibility and appropriateness of questions. This process has involved 12 revisions of YAYS for CYP and eight revisions of the YAYS parent/carer form, and has been trialled in three paediatric dermatology centres in London, Oxford and Nottingham with 354 CYP and 29 parent/carer forms completed to date. The YAYS development process, findings from YAYS and feedback will be presented. The purpose of YAYS is to enable CYP to raise issues and guide clinicians in evaluation and discussion of mental health concerns in CYP, collaboratively as part of their clinic appointment. Feedback has been very positive from CYP who feel empowered that the impact of their skin disease on all areas of wellbeing can be addressed.

Honesty-humility predicts humanitarian prosocial behavior via social connectedness: A parallel mediation examining connectedness to community, nation, humanity, and nature.

Abundant psychological research demonstrates bivariate relationships between individual prosocial propensity, intrinsic connectedness to various social domains, and the enactment of prosocial behavior. More recently, "connectedness" has been positioned as a necessary domain-specific motivator for enacting prosocial propensity in relevant domains of concern. Our study offers novel contributions to this theory by examining the role of connectedness in driving prosocial behavior within a humanitarian domain in which behavior is enacted in the interest of the global community. Additionally, our research uniquely considers connectedness to multiple social domains concurrently, in order to disentangle any covariance that may be impacting research findings. A parallel mediation model with four mediators (connectedness to community, nation, humanity, and nature) was applied to better understand the effect of prosocial propensity (honesty-humility) on humanitarian prosocial behavior. The cross-sectional survey was completed by 715 Australian adults, and support was found for mediation via each of the mediators. Specifically, connectedness to community, to humanity, and to nature each positively affected the relationship between prosocial propensity and prosocial behavior, with the largest effect via connectedness to humanity. Contrastingly, connectedness to nation negatively mediated this relationship, despite being positively correlated with these constructs. These findings highlight the individual difference underpinnings of humanitarian prosocial behaviors and the role of connection to relevant domains in behavior actualization. Fostering connectedness to humanity remains desirable to promote global prosocial outcomes, with future research needed on the interplay between these related levels of connectedness and between connectedness to one's nation and global humanitarian engagement.