Group Members Research Articles

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
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.
Methods
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.
Review results
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.
Discussion
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.

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 >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 < 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 < 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.

Adductor Laryngeal Dystonia Versus Muscle Tension Dysphonia: Examining the Utility of Automated Acoustic Analysis to Detect Task Dependency as a Distinguishing Feature.

Differentiating adductor laryngeal dystonia (ADLD) and primary muscle tension dysphonia (pMTD) can be challenging. Unlike pMTD, ADLD is described as "task-dependent" with voiced phonemes purportedly provoking greater sign expression than voiceless phonemes. We evaluated the ability of two automated acoustic measures, the Cepstral Spectral Index of Dysphonia (CSID) and creak, to detect task dependency and to discriminate ADLD and pMTD. CSID, % creak, and listener ratings of dysphonia severity were obtained from audio recordings of patients with ADLD (n = 29) or pMTD (n = 33) reading two sentences loaded with either voiced or voiceless phonemes. Group × Sentence Type interaction effects confirmed that both "normalized" CSID and % creak detected task-dependent sign expression in ADLD (i.e., worse symptoms on the voiced- vs. voiceless-loaded sentence). However, a stepwise binary logistic regression analysis with group (ADLD vs. pMTD) as the dependent variable and % creak and normalized CSID variables (voiced, voiceless, and voiced vs. voiceless difference) as covariates revealed that the normalized CSID voiceless-laden sentence z score was the only significant predictor of group membership. Estimates of diagnostic precision from the normalized CSID voiceless sentence z scores were superior to % creak or listener ratings. Finally, the CSID possessed the strongest correlations with listener severity ratings regardless of group or sentence type. Although both normalized CSID and % creak detected task-dependent performance as a distinguishing feature of ADLD, a CSID profile wherein (a) the voiceless sentence z score was less severe than the voiced sentence and (b) the normalized voiceless sentence z score was within approximately 2 SDs (or less) of typical expectations provided the best estimates of diagnostic precision. Automated acoustic measures such as the CSID and creak provide useful information to objectively discriminate ADLD and pMTD.

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.

Beyond binary group categorization: towards a dynamic view of human groups

ABSTRACT Society is a composite of interacting people and groups. These groups play a significant role in maintaining social status, establishing group identity and social identity, and enforcing norms. As such, groups are essential for understanding human behavior. Nevertheless, the study of groups in everyday group life yields many diverse and sometimes contradicting theories of group behavior, and researchers tend to agree that we have yet to understand the emergence of groups out of aggregates of individuals. The current paper aims to shed new light on the convoluted interrelation between groups and individuals by focusing on individuals’ social identities and group categorization. It does so by exploring the dynamic nature of the self and its implications on identity and group membership, and introducing a framework recognizing the fluidity of groups and group categorization. Incorporating historical insights with contemporary theories, this paper argues for a flexible understanding of group dynamics that surpasses rigid in-group and out-group classifications, proposing instead that group affiliations exist along a continuum that reflects the ever-changing social landscape.

Is There a Need to Expand the Refugee Definition?

This article examines the urgency of protecting against displacement caused by climate change under international law and questions why Rohingya displacement has not been resolved even though it has been protected by international law, whether the harmonization of human rights approaches, national law, and international law towards refugees and climate refugees. This paper uses a literature review method and a normative legal approach by reviewing national and international human rights law. The findings of this paper suggest that many Rohingya continue to flee to safer countries, often taking enormous risks, including dangerous ocean crossings. The impacts of climate change can be categorized as persecution; the Refugee Convention requires that such persecution be due to race, religion, nationality, political opinion, or membership of a particular social group. Persecution alone is not enough. The difficulty is that the impacts of climate change are largely indiscriminate and not related to specific characteristics such as a person's background or beliefs. The Rohingya people have experienced violence, discrimination, and persecution for decades, and the international community has failed to protect them effectively. The international community has allowed Myanmar to carry out genocide against the Rohingya without taking any action, even though United Nations member states are obliged to protect their population from genocide, war crimes, ethnic cleansing, and crimes against humanity. National, international, and human rights laws play an important role in determining the legal status and rights of refugees in a country. All three must be embedded and integrated, starting from international law and its derivatives to national law, which contains humanitarian rights.

Determinants of livelihood vulnerability to climate change among the agrarian community: Empirical evidence from the coastal region of Indian Sundarban Biosphere Reserve

The impacts of climate change on the livelihood of agrarian households on the coast of the Indian Sundaraban Biosphere Reserve have not been addressed. Thus, it is important to identify the underlying factors for livelihood vulnerability in this region and know how climate change-related events and socio-economic conditions affect them. This work aimed to measure six major domains of livelihood vulnerability, such as overall livelihood loss, livestock loss, crop and crop area loss, fishing area loss, housing loss, and income loss. I selected the ordered probit regression and focus group discussion approaches. Results revealed that the significant determinant factors of livelihood vulnerability were early warning system, education, land holding size, social group, type of house, occupation, distance to the local market and health centre, membership of self-help group, agreement with summer temperature increased, cyclone increased, flood increased, drought in Kharif season increased. In this region, several agrarian households have taken some adaptation measures such as changing crop varieties, crop diversification, converting agriculture to fishery, and saline tolerant varieties. This approach is adaptable and can be put into practice on different levels, not just in other parts of India but also in diverse developing countries.

Group based trajectory modeling identifies distinct patterns of sympathetic hyperactivity following traumatic brain injury.

Background Paroxysmal Sympathetic Hyperactivity (PSH) occurs with high prevalence among critically ill Traumatic Brain Injury (TBI) patients and is associated with worse outcomes. The PSH-Assessment Measure (PSH-AM) consists of a Clinical Features Scale (CFS) and a Diagnosis Likelihood Tool (DLT), intended to quantify the severity of sympathetically-mediated symptoms and likelihood that they are due to PSH, respectively, on a daily basis. Here, we aim to identify and explore the value of dynamic trends in the evolution of sympathetic hyperactivity following acute TBI using elements of the PSH-AM. Methods We performed an observational cohort study of 221 acute critically ill TBI patients for whom daily PSH-AM scores were calculated over the first 14 days of hospitalization. A principled group-based trajectory modeling approach using unsupervised K-means clustering was used to identify distinct patterns of CFS evolution within the cohort. We also evaluated the relationships between trajectory group membership and PSH diagnosis, as well as PSH DLT score, hospital discharge GCS, ICU and hospital length of stay, duration of mechanical ventilation, and mortality. Baseline clinical and demographic features predictive of trajectory group membership were analyzed using univariate screening and multivariate multinomial logistic regression. Results We identified four distinct trajectory groups. Trajectory group membership was significantly associated with clinical outcomes including PSH diagnosis and DLT score, ICU length of stay, and duration of mechanical ventilation. Baseline features independently predictive of trajectory group membership included age and post-resuscitation motor GCS. Conclusions This study adds to the sparse research characterizing the heterogeneous temporal trends of sympathetic nervous system activation during the acute phase following TBI. This may open avenues for early identification of at-risk patients to receive tailored interventions to limit secondary brain injury associated with autonomic dysfunction and thereby improve TBI patient outcomes.

Determinants of smallholder membership in farmers’ groups in the pigeon pea-based farming system in Uganda

BackgroundMembership in farmers’ group (FGs) is an important social aspect of agricultural development. Little is known about the relative importance and benefits from participation in FGs in Uganda, specifically for pigeon pea smallholders in northern Uganda.MethodsWe conducted a cross-sectional survey with 257 smallholders to examine the factors that motivate farmer’s group membership and its influence on pigeon pea yield and technical efficiency (TE) in northern Uganda. We applied the Stochastic Production Frontier (SPF) model and complementary models to assess membership impact on pigeon pea yield and TE determinants, respectively. TE is defined as the degree to which smallholders use the minimum feasible farm inputs to produce a given level of pigeon pea grain.ResultsResults show that FG members were generally older and more experienced, and had better access to extension services (76%) and credit (43%) compared to non-members. Smallholders’ age, access to agricultural training, extension services, and the distance travelled to market centres were statistically significant (p < 0.05), thus limiting the likelihood of FG membership. TE for both groups was low and quite similar, at 63% and 59% for members and non-members, respectively, implying that both groups did not use the available farm resources to maximize pigeon pea yield.ConclusionOur results confirm that FG membership enhances smallholder’s access to extension, agricultural training services and credit. For northern Uganda, the study provides recommendations for increased government investment in the human, financial and physical capacity of extension agents as a strategy for rural development and improved livelihoods. In addition, policy incentives can encourage farmers to seek membership in farmers’ groups and related associations.

Free-listing and Semantic Knowledge: A Tool for Detecting Alzheimer Disease?

Impairment in semantic knowledge contributes to Alzheimer disease (AD)-related decline. However, the particulars of the impact AD has on specific domains of knowledge remain debatable. To investigate the impact of AD on specific semantic categories that are integral to daily functions-living things and man-made objects. We administered a free-listing task (written version) to 19 individuals with AD and 15 cognitively normal older adults and assessed the task's relationship with other cognitive and functional tests in clinical use. We compared the contents of the lists of salient concepts generated by the AD and control groups. Group membership (AD or control), after controlling for age, sex, formal education, and an estimate of premorbid intellectual ability, predicted the groups' performance on the free-listing task across two categories. Functional status was inversely related to performance on the free-listing task, holding demographic variables constant. Based on a comparison of the contents of the free lists that were generated by the two groups, it was possible to conclude that, in individuals with AD, conceptual knowledge central to the respective categories was well preserved, whereas the peripheral conceptual material showed evidence of degradation. The free-listing task, which is an easy-to-administer and cost-effective tool, could aid in the preliminary detection of semantic knowledge dysfunction, revealing concepts that are better preserved and, possibly, the characterization of AD. Cognitive assessment tools that can be applied across cultures are needed, and the free-listing task has the potential to address this gap.

Workplace inclusion in higher education philanthropy

Positive workplace inclusion is important for any business today, but especially so for higher education philanthropy. Higher education philanthropy suffers from high turnover and lack of diversity, making successful workplace inclusion urgent. Higher education philanthropy exacerbates low diversity and high turnover through education bias, compensation and opportunity imbalance, work culture expectations that favour in-group members, low task interdependency, isolation from co-workers, low support from leadership, and burnout. To address the primary challenge of turnover, this paper proposes unit-level measures that higher education philanthropy offices might use to increase inclusion, promote diversity and improve work group performance and morale.

Miesize: Effect-size calculation in imputed data

In this article, I describe the miesize command for the calculation of effect sizes in imputed data. There may be situations where an effect size needs to be estimated for an intervention, an exposure, or a group membership variable but data on the independent or dependent variable are missing. Such missing data are commonly dealt with by multiply imputing plausible values. However, in this circumstance, the estimated effect size and associated standard errors will need to be pooled and estimated from the imputed dataset. The miesize command automates this process and calculates effect sizes for a binary variable from multiply imputed data in wide format. The estimates and standard errors (used to calculate the confidence intervals) are recombined using Rubin’s (1987, Multiple Imputation for Nonresponse in Surveys [Wiley]) rules. These rules are applied such that the average point estimate for the effect size is calculated from the imputed datasets. The pooled standard error, and hence confidence intervals, is calculated to account for both the variance between the imputed datasets and the variance within them. Pooled effect sizes and confidence intervals for Cohen’s (1988, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. [Lawrence Erlbaum]) d, Hedges’s (1981, Journal of Educational Statistics 6: 107-128) g, and Glass’s ( Smith and Glass, 1977 , American Psychologist 32: 752-760) delta are provided by miesize.