Group Members Research Articles

Determinants of access to rural financing in the democratic republic of Congo: The case of Kongo central

This article aims to identify the determinants influencing access to rural finance, focusing on a sample of 500 family farms in Kongo Central. This study revealed that membership in a financial solidarity group and awareness of the existence of a local development committee significantly influences access to credit using Probit and Logit regression methods. This study highlights the importance of supply and demand factors in accessing rural finance. Key demand-related determinants include gender, marital status, sector of activity, household size and high levels of education. This research suggests strengthening policies aimed at increasing financial education and women's empowerment to improve access to financing. These measures could enable more individuals especially women to effectively access and use financial services. By addressing these determinants can help policymakers better design interventions to support rural development in this province. The results underscore the need for a holistic approach considering both supply and demand factors to improve financial inclusion for rural populations.

Festive culture, social hierarchization, and urban inequality: Analysis of the fallas of València (1981–2019)

ABSTRACT Festive rituals have been analyzed as rituals of interaction with great capacity to generate group membership and economic impact. However, the conflictual dimension of these rituals should be taken into account, as festive rituals are also spaces of conflict and exclusion. In this sense, ritual festivities can lead to urban cohesion and the generation of local identities, but they can also reinforce internal social divisions, reproducing social inequalities and expressing the social hegemony of the dominant elites. In this sense, the fallas of Valencia constitute a carnivalesque festive ritual that shows exactly these effects, since the organization of the fallas commissions represents one of the city’s principal forms of social participation and cohesion, but at the same time reproduces the social hierarchization of urban districts. Thus, one of the main formats is found in the stratification by neighborhoods. The fallas reflect the economic strength of the traditionally socially dominant neighborhoods, but also show the emergence of new high-income neighborhoods, with new commissions defined by high resources. In this way, studying the fallas is a way of studying the urban hierarchy of Valencia, as they have gone hand in hand throughout the period studied.

Is adoption of modern dairy farming technologies interrelated? A case of smallholder dairy farmers in Meru county, Kenya

Attempts have been made to promote the adoption of modern dairy farming technologies (MDFT). However, the adoption of these technologies largely remains underutilized. This study aimed to analyze the determinants of the adoption of MDFTs in Meru County. Using purposive and proportionate sampling techniques from 355 smallholder dairy farmers in Meru County, Kenya, this study analyzed the factors that facilitate or impede the adoption of MDFTs. We use a Multivariate probit (MVP) to evaluate adoption decisions by dairy farm households facing multiple MDFTs. The results reveal a significant correlation among the eight MDFTs suggesting that modern technologies are interrelated. The MVP model results indicate that household income, education level, number of extension contacts, access to credit, farmer group membership, farming experience and livestock monetary value had positive effect on the adoption of MDFTs. Education level and extension contacts had a positive influence on the adoption of fodder establishment and preservation technologies. Farming experience in dairy farming had a positive effect on the adoption of well-structured and clean sleeping areas, and the growth of Rhodes grass. Household income had a positive effect on the growth of Rhodes grass and feed mixture. This work illustrates a need for a policy implication and insight into a need for the county government and private milk processing companies to increase extension frequency to enhance the adoption of MDFTs. Additionally, there is a need to increase access to affordable credit, this should be considered by the government by establishing strengthening a smallholder low-interest and efficient local credit schemes and institutions.

Suicidal Behavior and Personality Group Affiliation: Approaches and Research Directions

Introduction. As it follows from a number of scientific works, not only suicide rates, but also their motives and ways of committing suicide vary widely in different countries, strata of society and strata of the population. In recent decades, research on this issue has become interdisciplinary and cross-cultural. Suicidal behavior is increasingly considered in the context of the suicidal person’s belonging to a certain social group or cultural community. Objective. To analyze the impact of an individual’s membership in a large social group or cultural community on suicidal behavior. The main directions of suicidal behavior research. In the vast array of theoretical and research works the following directions of suicidal behavior study can be distinguished: existential, clinical, socio-demographic, socio-cultural and socio-psychological. Starting from E. Durkheim’s sociological etude “Suicide”, the group affiliation of an individual is considered as a leading factor of suicidal behavior. However, the sociological approach appears limited because its proponents place excessive emphasis on demographic variables rather than on the psychological significance of suicide and the values of the individual prone to suicidal behavior. A. Adler’s socio-psychological conceptualization of suicide is partly responsible for overcoming these limitations. Adler’s socio-psychological concept of suicide, which takes into account not only demographic indicators, but also how suicides perceive, evaluate and experience their own group affiliation and the values of their social group. According to A. Adler, the predictors of suicide are a sense of community (belonging) and social interests of the individual. Sense of community and group belonging as a factor of suicidal behavior. Comparison of studies conducted in three countries (Russia / USSR, Japan and the USA) allows us to identify two socio-psychological factors of suicidal behavior: 1) negative attitude to one’s own group or cultural identity; 2) hypertrophy of the sense of community and belonging, leading to a decrease in the value of one’s own personality and individual life. Discussion. As modern cross-cultural studies show, suicidal behavior can be observed both at the deficit of the sense of community and group belonging and at the high level of development of this sense. This is conditioned, on the one hand, by the attitude of a suicide victim to his/her social group and cultural identity, and, on the other hand, by the historically formed attitude of society to suicide.

Managing forest residues for biodiversity, bioenergy, and smoke reduction: Insights from a Discrete Choice Experiment in Tasmania, Australia

The management of forest residue following timber harvesting is an important global issue. If forest residues are not managed appropriately, they can increase the risk of catastrophic wildfires, negatively impact wildlife and aesthetic values. These risks can be reduced if forest residues are used for bioenergy production, which can also generate jobs, but using forest residues for bioenergy production may adversely impact biodiversity. We explore public preferences relating to the varied impacts of forest residue management outcomes using data collected for a Discrete Choice Experiment in Tasmania, Australia. On average, respondents were willing to pay AUD 34, AUD 29, and AUD 14 per year, respectively, to reduce smoke emissions, increase the abundance and diversity of invertebrate species, and create employment. This suggests that Tasmanian forest managers may find public support for residue management practices that include the production of at least some bioenergy products. Moreover, the social benefits of the state-wide harvesting of forest residues for bioenergy could be positive even with relatively high costs. WTP varied by age, education, environmental group membership, invertebrate fear, and wildfire exposure. The influence of these factors varies across regions, offering insights into forest residue management for regions with similar socioeconomic and environmental contexts.

Means to an end: Characteristics and follow-up of emergency department patients with a history of suicide attempt via medication overdose.

Availability and accessibility of a wide range of medications may be a contributing factor to rising medication-related overdose (OD) rates. Treatment for both suicide attempts (SAs) and ODs often occurs in the emergency department (ED), highlighting its potential as a screening and intervention point. The current study aimed to identify sociodemographic and clinical characteristics of individuals who reported SA via medication OD compared to other methods and to examine how these patients' suicide severity and behaviors differed over 12-month post-ED follow-up. Data were analyzed from Phases 1 and 2 of the Emergency Department Safety Assessment and Follow-up Evaluation multicenter study (N = 1376). Participants with a history of SA (n = 987) were categorized based on whether they indicated a past medication-related SA via OD. Of participants with history of SA, 62.7% (n = 619) reported medication OD for either their most serious or their most recent SA. Multivariate analyses indicated female birth sex, diagnosis of bipolar disorder, and having some college education were significantly associated with membership in the medication OD attempt group (p <0.05). Of those who attempted suicide over the 12-month follow-up, nearly 60% of participants in the medication OD attempt group reported a subsequent SA via OD over follow-up. However, nearly half (46.5%) of participants with no medication OD at baseline also reported medication OD at follow-up. Among patients presenting to the ED, females, individuals with bipolar disorder, and patients with a college education, respectively, may be at highest risk for SAs via medication OD. Prospectively, medication OD appears to be a frequent method, even among individuals with no prior attempt via OD, as demonstrated by the high percentage of patients who did not have a medication OD at baseline, but reported a medication OD during follow-up.

Trajectories of work disability among individuals with anxiety-, mood/affective-, or stress-related disorders in a primary healthcare setting

Abstract Background Anxiety-, mood/affective-, or stress-related disorders affect up to one-third of individuals during their lives and often impact their ability to work. This study aimed to delineate trajectories of work disability (WD) among individuals diagnosed with anxiety-, mood/affective-, or stress-related disorder in primary healthcare and to examine associations between trajectory group membership and sociodemographic, clinical, and clinical-related factors. Methods The study population included working-age individuals, aged 22–62 years, living in Stockholm County, Sweden, who experienced a new episode of any anxiety-, mood/affective, or stress-related disorder in primary healthcare in 2017 (N = 11,304). Data were obtained from Swedish national and regional registers and were linked using pseudonymised unique personal identification numbers. The primary outcome was days with WD (sum of sickness absence and disability pension days) during the three years before and three years after a diagnosis of anxiety-, mood/affective-, or stress-related disorders in primary healthcare. A zero-inflated Poisson group-based trajectory model was used to identify groups of individuals with similar patterns of WD over the study period, with a multinomial logistic regression used to examine associations of sociodemographic, clinical, and clinical-related factors with trajectory group membership. Results Four distinct trajectory groups were found, high increasing (5.1%), with high levels, from 16 to 80 days of WD in six-monthly intervals during follow-up, peak (11.1%), with a peak in WD, up to 32 days of WD, around the time of the diagnosis, low increasing (12.8%), with an increase in days of WD from 4 to 22 during the study period, and constant low (71.1%), with almost no WD over the study period. In multinomial regression models, diagnostic category, psychotropic medication use, a diagnosis of a psychiatric disorder within secondary healthcare, age at diagnosis, and occupation were associated with WD trajectory groups. Conclusions Around two-thirds of individuals treated for a new episode of any anxiety-, mood/affective-, or stress-related disorder in primary healthcare have an excellent prognosis regarding WD. Several sociodemographic and clinical characteristics were associated with group membership; these factors could identify individuals at risk of long-term welfare dependency and who might benefit from interventions to promote a return to work.

Social cure model: testing the link between identity centrality and body appreciation in diverse sexual orientation and gender identity groups

BackgroundThe level of experienced sociocultural pressure to have an idealized body can vary depending on a person’s gender identity and sexual orientation. The current study explored whether differences in levels of body appreciation among people with different sexual orientations and gender identities vary because of differing levels of experienced pressure by in-group members and varying levels of experienced hostile behaviors because of their looks or body. Thereby, the study tests the social cure model, according to which high levels of identity centrality are associated with better mental health.MethodsAn online cross-sectional questionnaire study was conducted with 1,587 people (51.3% cisgender women, 39.3% cisgender men, 9.5% non-binary; 52.9% identified as heterosexual, 27.7% identified as bisexual/pansexual, 11.2% identified as gay/lesbian, 8.2% identified as asexual/demi/queer; Mage = 32.9, SD = 12.6) from German-speaking countries. Variables were assessed with German-language versions of the Multidimensional and Multicomponent Measure of Social Identification, Body Appreciation Scale-2, the Perceived Stigmatization Questionnaire, and the Sociocultural Attitudes Towards Appearance Questionnaire-4, revised. A manifest-path model was calculated.ResultsNon-binary persons reported lower levels of body appreciation than did cisgender men and sexual minority persons reported lower levels of body appreciation than did heterosexual persons. Furthermore, sexual minority persons experienced more hostile behaviors directed towards them because of their looks or body than did heterosexual persons. Similarly, non-binary persons experienced more hostile behaviors than did men. Non-binary persons were subjected to lower levels of in-group pressure than were men. Gay/lesbian persons and asexual persons were subjected to lower levels of in-group pressure than were heterosexual persons. More hostile behaviors and stronger in-group pressure were related to lower body appreciation. In cisgender women and men indirect links revealed associations between strong identity centrality and low levels of body appreciation through the mediator of high in-group pressure.ConclusionsData in sexual minority individuals or non-binary persons supported the social cure model, according to which persons can find support and validation for their looks and body from in-group members. In cisgender women and men, strong identification as a woman or man can be related to stronger in-group pressure and in turn to lower body appreciation.

Operationalizing the social capital of collaborative environmental governance with network metrics

Abstract Background&amp;#xD;Social capital is frequently invoked as a reason for engaging in collaborative environmental governance. Yet we have a limited understanding of how collaborative environmental governance mobilizes different types of social capital and how the advantages and costs of social capital accrue for different groups of people. Explicit measures of social capital, such as through social network methods, will help build an understanding of how social capital facilitates collective processes and for whom.&amp;#xD;Methods&amp;#xD;We reviewed highly cited articles in Web of Science and Scopus using “social capital” as the search term to identify foundational and emergent social capital concepts. In the context of collaborative environmental governance, we operationalized these social capital concepts with network measures drawn from our expertise and highlighted existing empirical relationships between such network measures and collaborative outcomes.&amp;#xD;Review results&amp;#xD;We identified two different camps of social capital concepts - one based on social relations that could be readily operationalized with social network measures and the other based on actor characteristics that can further contextualize network data. Relational social capital concepts included social relations among actors; the collective social setting in which relations are embedded; and the advantages and costs that social capital confers to individuals and the collective. Social capital concepts based on actor characteristics included socio-cognitions (e.g., trust, norms, identification with a group, shared meanings) and community engagement (e.g., group membership, civic participation, volunteerism). Empirical evidence using social network approaches to measure social capital reveals patterns in relationship building that influence collaborative and other sustainability outcomes.&amp;#xD;Discussion&amp;#xD;Social network approaches described here may help define and quantify the social capital mobilized by collaborative governance. Additional research is necessary to track the social capital of collaboratives over time, link it to outcomes, and better understand the social justice implications of collaborative governance.

The impact of social identity complexity on intergroup parochial and universal cooperation under different payoff structures and frames

As society evolves, individuals increasingly cooperate with both in-group members and out-group strangers, despite risks such as betrayal. Social identity plays a crucial role in motivating this cooperation, significantly shaping cooperative behavior. This study explores how social identity complexity—arising from the overlapping of multiple social identities—affects intergroup cooperation. Using the Intergroup Parochial and Universal Cooperation (IPUC) game, we examined universal cooperation, weak parochial cooperation, and strong parochial cooperation under different payoff structures—the equal outcomes game (EOG) and the collective incentives game (CIG)—and framing conditions (individual and group frames). The findings reveal that social identity complexity is positively related to universal cooperation and negatively related to strong parochial cooperation. Individuals with high social identity complexity demonstrated higher levels of universal cooperation and lower levels of strong parochial cooperation, particularly within the CIG compared to the EOG. Additionally, individuals with high social complexity showed greater universal cooperation and less strong parochial cooperation in the individual frame compared to the group frame, while those with low social identity complexity exhibited more weak parochial cooperation in the individual frame. These findings suggest that higher social identity complexity fosters intergroup cooperation, with different payoff structures and framing conditions significantly influencing cooperative behavior.

A Remote Patient Monitoring System with Feedback Mechanisms using a Smartwatch: Concept, Implementation and Evaluation based on the activeDCM Randomized Controlled Trial.

Technological advances allow recording and sharing of health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable a dense management of individual health trajectories, monitoring of risk factors and asynchronous feedback. However, most Remote Patient Monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack the patient-centric design. The objective was to conceptualize and implement a user-friendly, reusable, interoperable and secure RPM system incorporating asynchronous feedback mechanisms, using a broadly available consumer wearable (Apple Watch). Additionally, the study sought to evaluate factors influencing patient acceptance of such systems. The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach, ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was employed as part of the activeDCM prospective, randomized, interventional study focusing on Dilated Cardiomyopathy (DCM). Each patient used the system for at least 12 months. The System Usability Scale (SUS) was employed to measure usability from a subjective patient perspective. Additionally, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data, classified into Sensor-based Health Data (SHD) and Patient Reported Outcome Measures (PROM). Descriptive statistics using boxplots, along bootstrapped multiple linear regression with a 95% confidence interval (CI) were utilized for evaluation, analyzing the influence of age, sex, device experience and intervention group membership. The RPM system consists of four interoperable components: patient-devices, data-server, data-viewer and notification-service. The evaluation of the system was conducted with 95 consecutive DCM patients (female: 28 of 95 (29%), age: 50±12 years) completing the activeDCM study protocol. The wearable/ smartphone application of the system achieved a mean SUS score of 78±17, which was most influenced by device experience. 83 of 95 patients (87%) could integrate the wearable application (very) well into their daily routine and 67 of 95 (70%) saw a benefit of the RPM system for management of their health condition. Patients interacted on average with the wearable on 61%±26% of days enrolled in the study, corresponding to 239±99 of 396±39 days. SHD was available on average for 78%±23% of days and PROM data 64%±27% of weeks enrolled in the study, corresponding to 307±87 of 396±39 days and 35±15 of 56±5 weeks, respectively. Wearable interaction frequency, SHD and PROM completeness were most influenced by intervention group membership. Our results mark a first step towards integrating RPM systems, based on a consumer wearable device for primary patient input, into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable and secure RPM system, that can be integrated into patients' daily routines. ClinicalTrials.gov-Identifier: NCT04359238.

B - 143 A Comparison of Structured Inventory of Malingered Symptomology (SIMS) Performance to Multiple Validity Measures in a Criminal Forensic Sample

Abstract Objective We compared valid or invalid SIMS performance to other performance and symptom validity measures. Methods Data from 119 forensic neuropsychological evaluations [competency (n = 106), forensic (n = 5), insanity (n = 5), commitment review (n = 2), and danger (n = 1)] were analyzed. Three chi-squares tests compared group membership (valid/invalid) for SIMS total score, affective (AF) subscore, or low intelligence (LI) subscore to a separate valid or invalid group membership based on MMPI-2-RF (VRIN-r and TRIN-r), WAIS-IV Reliable Digit Span, and WMT (IR, DR, and CONS) validity cutoff scores (74% failed &amp;gt;2 measures). Results Sample was predominately male (91.6%) and Caucasian (53.8%). Mean age was 35.1 years; mean education was 11.36 years. For SIMS total, 36% failed the SIMS but passed all other validity measures [χ 2(1) = 21.668; p &amp;lt; 0.001; V = 0.427] for a 64% concordance rate for overall valid performance. Individuals with valid scores on the SIMS AF subscale were likely (66%) to have valid performance on all other validity measures [χ 2(1) = 12.418; p &amp;lt; 0.001; V = 0.323]. Individuals with valid scores on the SIMS LI subscale were essentially at chance (52%) to have performed validly overall [χ 2(1) = 9.406; p = 0.002; V = 0.0.281]. Conclusions In this forensic criminal sample, a third of individuals failed the SIMS total score and AF subscale while having credible performance otherwise. Approximately half failed the LI subscale while passing other validity measures. Results suggest that SIMS performance is not strongly related to SVT measures that focuses on response consistency or PVT performance; there is still significant classification overlap. However, high base rate of PVT/SVT failure in this sample may limit generalizability.

Assessing physical fitness adaptations in collegiate male soccer players through training load parameters: a two-arm randomized study on combined small-sided games and running-based high-intensity interval training.

To evaluate the effects of a 4-week intervention combining small-sided games (SSGs) and high-intensity interval training (HIIT) on physical fitness in collegiate male soccer players. Twenty-one soccer players were randomly assigned to either the HIIT + SSGs group (n = 11) or a control group (n = 10). Physical fitness was assessed at baseline and 1-week post-intervention, including countermovement jump (CMJ), change of direction (COD) test, sprint test, repeated sprint ability (RSA) test, and 30-15 Intermittent Fitness Test (30-15IFT). The intervention comprised eight sessions over 4weeks: four SSGs and four HIIT. The intervention group showed small to moderate improvements: mean RSA improved by 4.5% (p = 0.07), CMJ increased by 3.2% (p = 0.12), and 30-15IFT scores enhanced by 6.8% (p = 0.09). Key predictors of group membership included heart rate load per minute (OR 1.602) and various GPS variables. The 4-week intervention combining SSGs with HIIT did not produce statistically significant improvements in most physical fitness variables compared to the control group. Although there were positive trends in variables such as RSA and 30-15IFT, these changes were modest and not statistically significant. The results suggest that while the combined SSGs and HIIT approach shows potential, its impact on physical fitness over a 4-week period is limited, with some variables, like CMJ, even showing decreases.

Analyzing Engagement: A Study of Teething-Related Discussions and Trends on Facebook.

Caregivers often use Facebook to seek advice on children's health issues, including teething. However, the lack of professional oversight on this platform can lead to the spread of misinformation, emphasizing the need for research on user engagement with teething content. This longitudinal study assesses public interaction with teething-related information on Facebook, aiming to inform children's health organizations about creating accurate social media content. A year-long analysis of English-language Facebook posts tagged "teething" was conducted, focusing on public pages and groups, while excluding unrelated or private content. Sources were categorized, and engagement was measured against page likes or group memberships. Chi-square and Kruskal-Wallis tests were used to evaluate statistical differences in post types and engagement by source (P < 0.05). Out of 193 relevant posts, interactive content was the most common (112 posts (58%)), followed by miscellaneous (59 posts (30.6%)) and educational content (22 posts (11.4%)). Noticeable confusion among users was observed concerning teething symptoms and processes. Misinformation was prevalent, particularly regarding the use of amber necklaces, amber teething toys, topical anesthetics, and nocturnal bottle feeding for relief. The engagement rate for teething content was low at 1.09%, with significant variations in content strategies by source (P < 0.001), though engagement rates were consistent (P = 0.406). The study uncovers active but misinformed discussions about teething on Facebook, marked by a lack of evidence-based advice and low engagement rates. These findings emphasize the immediate need for children's health organizations to provide scientifically accurate information to foster a better-informed online community.

Prediction of Employees’ Addiction Potential Based on Some of the Most Important Psychological Variables: A Discriminant Analysis Study

Background: Drug addiction significantly impacts employee performance, leading to increased absenteeism, reduced productivity, and higher rates of occupational accidents. Therefore, identifying predictive factors for addiction potential is crucial in preventing drug dependence. Objectives: This study aimed to develop a group membership model using discriminant analysis to predict employees' potential for addiction based on key psychological variables. Methods: A correlational study was conducted with a statistical population consisting of all employees working in public departments in Bojnord City, Iran, in 2021 (N = 2,837). A random sample of 303 employees was selected to complete the Iranian Form of the Addiction Potential Scale, the Big Five Inventory, the Cognitive Emotional Regulation Questionnaire, and the Generalized Self-Efficacy Scale. Data analysis was performed using the discriminant analysis method in SPSS software version 23. Results: The findings revealed that employees' addiction potential can be predicted using personality and cognitive variables. The discriminant analysis equation effectively distinguishes employees with high and low addiction potential based on psychological variables, including extroversion, neuroticism, self-efficacy, and cognitive emotional regulation. Conclusions: The study concluded that self-efficacy, cognitive emotional regulation, neuroticism, and extroversion are significant predictors of addiction potential. It is recommended to incorporate these variables into training courses and personnel selection processes for government jobs.

The impact of multilingualism and socio-economic status on academic performance: evidence from the SCAMP and the national pupil databases

ABSTRACT This study examines the impact of multilingualism and socioeconomic status on academic performance within the UK, utilising data from 3,213 pupils from the National Pupil Database who also took part in the Study of Cognition, Adolescents and Mobile Phones (SCAMP). We employed multilevel modelling to analyse the relationship between language experience, socioeconomic status and Key-Stage 2 (KS2∼11 years) and Key-Stage 4 (KS4∼16 years) performance in English, Mathematics and Science. Findings reveal that multilingual learners initially face academic challenges at KS2, particularly in English and Science, but achieve comparable results with monolingual peers by KS4, overcoming early setbacks. Notably, simultaneous multilinguals not only catch up but excel beyond their monolingual counterparts by KS4, demonstrating the significant long-term academic benefits of early multilingual exposure and /or its associated cultural factors. Further analysis indicates that multilingual group membership mitigated the adverse effects of low socioeconomic status, with pupils from these backgrounds making substantial academic strides between KS2 and KS4 compared to monolingual peers. This challenges prevalent misconceptions about multilingualism in education. These findings underscore the need for educational policies that harness linguistic diversity to foster academic equity and success, emphasising the crucial role of language experience and socioeconomic factors in shaping educational outcomes.

Latent profiles of disability in retired, community‐dwelling older adults

AbstractLater life disability poses a significant public health challenge, and while research often concentrates on physical limitations, understanding the full spectrum of disability requires a comprehensive approach. This study employs latent profile analysis (LPA), a statistical technique that identifies underlying subgroups or profiles within a larger population based on patterns of responses to multiple indicators, to classify disability. We examined one wave of existing panel data from 414 participants aged 72–106 living in retirement communities. We focus on the following disability indicators: basic and instrumental activities of daily living; physical, cognitive, and sensory impairments; and participation restrictions. Three distinct profiles emerged. The largest group (58%), “Low Disability,” exhibited minimal disability across all domains and superior psychological well‐being. The second group (38%) experienced “High Physical and Functional Disability,” with a higher prevalence among older, unmarried women. The smallest group (4%) displayed “High Cognitive‐Sensory‐Functional Disability,” marked by extensive cognitive impairment and the highest functional and sensory limitations, prevalent in advanced age with poorer psychological outcomes of all profiles. Notably, participation restrictions showed minimal variation across disability profiles. The findings emphasize the need for a data‐driven policy framework addressing disability profiles. Efficient resource allocation and targeted support are crucial, and prevention strategies should proactively address individuals at risk of transitioning into higher‐multidomain disability profile membership. Given the connection between disability and mental health, addressing these concerns can enhance overall health and well‐being. Future research should explore changes in disability group membership over time in diversified samples.

Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups

In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique that utilizes a low-dimensional latent representation. To faithfully reproduce unknown heterogeneity reflected in marginal distributions, we propose to combine VAEs with pre-transformations. For dealing with known heterogeneity due to sub-groups, we complement VAEs with models for group membership, specifically from propensity score regression. The evaluation is performed with a realistic simulation design that features sub-groups and challenging marginal distributions. The proposed approach faithfully recovers the latter, compared to synthetic data approaches that focus purely on marginal distributions. Propensity scores add complementary information, e.g., when visualized in the latent space, and enable sampling of synthetic data with or without sub-group specific characteristics. We also illustrate the proposed approach with real data from an international stroke trial that exhibits considerable distribution differences between study sites, in addition to bimodality. These results indicate that describing heterogeneity by statistical approaches, such as propensity score regression, might be more generally useful for complementing generative deep learning for obtaining synthetic data that faithfully reflects structure from clinical cohorts.

Cross-Cutting Exposure in Online News Comments: The Effects of Group Identification and Uncivil Discourse on Opinion Conformity and Expression

ABSTRACT Drawing upon the social identity model of deindividuation effects, this study investigated the effects of intergroup relations and discursive quality on individuals’ responses to comments that challenge their existing beliefs on news websites. A two-session online experiment with a 2 (group membership: in-group vs. out-group) × 2 (comment tone: civil vs. uncivil) between-subjects factorial design was conducted. The results showed that exposure to counter-attitudinal comments from in-group members, compared to out-group members, led to greater opinion conformity and less expression of disagreement. Additionally, there was a significant interaction between group membership and comment tone on disagreement expression. When counter-attitudinal comments were uncivil, individuals exposed to in-group comments expressed less dissent in their replies than those exposed to out-group comments. This tendency, however, was not significant when the counter-attitudinal comments were expressed in a civil tone.

Deep learning infused SIRVD model for COVID-19 prediction: XGBoost-SIRVD-LSTM approach.

The global impact of the ongoing COVID-19 pandemic, while somewhat contained, remains a critical challenge that has tested the resilience of humanity. Accurate and timely prediction of COVID-19 transmission dynamics and future trends is essential for informed decision-making in public health. Deep learning and mathematical models have emerged as promising tools, yet concerns regarding accuracy persist. This research suggests a novel model for forecasting the COVID-19's future trajectory. The model combines the benefits of machine learning models and mathematical models. The SIRVD model, a mathematical based model that depicts the reach of the infection via population, serves as basis for the proposed model. A deep prediction model for COVID-19 using XGBoost-SIRVD-LSTM is presented. The suggested approach combines Susceptible-Infected-Recovered-Vaccinated-Deceased (SIRVD), and a deep learning model, which includes Long Short-Term Memory (LSTM) and other prediction models, including feature selection using XGBoost method. The model keeps track of changes in each group's membership over time. To increase the SIRVD model's accuracy, machine learning is applied. The key properties for forecasting the spread of the infection are found using a method called feature selection. Then, in order to learn from these features and create predictions, a model involving deep learning is applied. The performance of the model proposed was assessed with prediction metrics such as R 2, root mean square error (RMSE), mean absolute percentage error (MAPE), and normalized root mean square error (NRMSE). The results are also validated to those of other prediction models. The empirical results show that the suggested model outperforms similar models. Findings suggest its potential as a valuable tool for pandemic management and public health decision-making.