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Introduction: Apart from a few studies with limited sample sizes, we have little data on attitudes toward lesbian and gay (LG) people in Greece. Methods: This study examines this topic in 949 heterosexual Greek participants. Based on previous research in cultural contexts other than Greece, we hypothesized that four demographics (gender, age, education, area of residence) and religious and political orientation predict a substantial amount of variance in homophobia (i.e., anti-LG attitudes). Results: We verified all observed variables except area of residence as significant predictors. Regarding the “intergroup contact hypothesis,” we distinguished the direct effects of the predictor variables from indirect effects mediated by contact with lesbians and gay men. All variables except area of residence showed a direct effect and, except for education, also an indirect effect on homophobia. The strongest effects were found for religious and political orientation, followed by gender. Highly religious, right-wing oriented, and male participants reported the highest levels of homophobia, partially mediated by their low level of contact with LG people. Discussion/Conclusion: The results confirm and further explain the detrimental role the Greek Orthodox Church, right-wing political parties, and traditional gender roles play in the acceptance of sexual minorities.
Background: Large language models (LLMs) are increasingly used in mental health, showing promise in assessing disorders. However, concerns exist regarding their accuracy, reliability, and fairness. Societal biases and underrepresentation of certain populations may impact LLMs. Because LLMs are already used for clinical practice, including decision support, it is important to investigate potential biases to ensure a responsible use of LLMs. Anorexia nervosa (AN) and bulimia nervosa (BN) show a lifetime prevalence of 1%-2%, affecting more women than men. Among men, homosexual men face a higher risk of eating disorders (EDs) than heterosexual men. However, men are underrepresented in ED research, and studies on gender, sexual orientation, and their impact on AN and BN prevalence, symptoms, and treatment outcomes remain limited.
Objectives: We aimed to estimate the presence and size of bias related to gender and sexual orientation produced by a common LLM as well as a smaller LLM specifically trained for mental health analyses, exemplified in the context of ED symptomatology and health-related quality of life (HRQoL) of patients with AN or BN.
Methods: We extracted 30 case vignettes (22 AN and 8 BN) from scientific papers. We adapted each vignette to create 4 versions, describing a female versus male patient living with their female versus male partner (2 × 2 design), yielding 120 vignettes. We then fed each vignette into ChatGPT-4 and to “MentaLLaMA” based on the Large Language Model Meta AI (LLaMA) architecture thrice with the instruction to evaluate them by providing responses to 2 psychometric instruments, the RAND-36 questionnaire assessing HRQoL and the eating disorder examination questionnaire. With the resulting LLM-generated scores, we calculated multilevel models with a random intercept for gender and sexual orientation (accounting for within-vignette variance), nested in vignettes (accounting for between-vignette variance).
Results: In ChatGPT-4, the multilevel model with 360 observations indicated a significant association with gender for the RAND-36 mental composite summary (conditional means: 12.8 for male and 15.1 for female cases; 95% CI of the effect –6.15 to -0.35; P=.04) but neither with sexual orientation (P=.71) nor with an interaction effect (P=.37). We found no indications for main effects of gender (conditional means: 5.65 for male and 5.61 for female cases; 95% CI –0.10 to 0.14; P=.88), sexual orientation (conditional means: 5.63 for heterosexual and 5.62 for homosexual cases; 95% CI –0.14 to 0.09; P=.67), or for an interaction effect (P=.61, 95% CI –0.11 to 0.19) for the eating disorder examination questionnaire overall score (conditional means 5.59-5.65 95% CIs 5.45 to 5.7). MentaLLaMA did not yield reliable results.
Conclusions: LLM-generated mental HRQoL estimates for AN and BN case vignettes may be biased by gender, with male cases scoring lower despite no real-world evidence supporting this pattern. This highlights the risk of bias in generative artificial intelligence in the field of mental health. Understanding and mitigating biases related to gender and other factors, such as ethnicity, and socioeconomic status are crucial for responsible use in diagnostics and treatment recommendations.
Background: Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. Recent studies using benchmark datasets and real-world social media data have demonstrated the capability of pretrained large language models in predicting suicidal ideation and behaviors (SIB) in speech and text.
Objective: This study aimed to (1) develop and implement ML methods for predicting SIBs in a real-world crisis helpline dataset, using transformer-based pretrained models as a foundation; (2) evaluate, cross-validate, and benchmark the model against traditional text classification approaches; and (3) train an explainable model to highlight relevant risk-associated features.
Methods: We analyzed chat protocols from adolescents and young adults (aged 14-25 years) seeking assistance from a German crisis helpline. An ML model was developed using a transformer-based language model architecture with pretrained weights and long short-term memory layers. The model predicted suicidal ideation (SI) and advanced suicidal engagement (ASE), as indicated by composite Columbia-Suicide Severity Rating Scale scores. We compared model performance against a classical word-vector-based ML model. We subsequently computed discrimination, calibration, clinical utility, and explainability information using a Shapley Additive Explanations value-based post hoc estimation model.
Results: The dataset comprised 1348 help-seeking encounters (1011 for training and 337 for testing). The transformer-based classifier achieved a macroaveraged area under the curve (AUC) receiver operating characteristic (ROC) of 0.89 (95% CI 0.81-0.91) and an overall accuracy of 0.79 (95% CI 0.73-0.99). This performance surpassed the word-vector-based baseline model (AUC-ROC=0.77, 95% CI 0.64-0.90; accuracy=0.61, 95% CI 0.61-0.80). The transformer model demonstrated excellent prediction for nonsuicidal sessions (AUC-ROC=0.96, 95% CI 0.96-0.99) and good prediction for SI and ASE, with AUC-ROCs of 0.85 (95% CI 0.97-0.86) and 0.87 (95% CI 0.81-0.88), respectively. The Brier Skill Score indicated a 44% improvement in classification performance over the baseline model. The Shapley Additive Explanations model identified language features predictive of SIBs, including self-reference, negation, expressions of low self-esteem, and absolutist language.
Conclusions: Neural networks using large language model–based transfer learning can accurately identify SI and ASE. The post hoc explainer model revealed language features associated with SI and ASE. Such models may potentially support clinical decision-making in suicide prevention services. Future research should explore multimodal input features and temporal aspects of suicide risk.
Background: As digital mental health delivery becomes increasingly prominent, a solid evidence base regarding its efficacy is needed.
Objective: This study aims to synthesize evidence on the comparative efficacy of systemic psychotherapy interventions provided via digital versus face-to-face delivery modalities.
Methods: We followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for searching PubMed, Embase, Cochrane CENTRAL, CINAHL, PsycINFO, and PSYNDEX and conducting a systematic review and meta-analysis. We included randomized controlled trials comparing mental, behavioral, and somatic outcomes of systemic psychotherapy interventions using self- and therapist-guided digital versus face-to-face delivery modalities. The risk of bias was assessed with the revised Cochrane Risk of Bias tool for randomized trials. Where appropriate, we calculated standardized mean differences and risk ratios. We calculated separate mean differences for nonaggregated analysis.
Results: We screened 3633 references and included 12 articles reporting on 4 trials (N=754). Participants were youths with poor diabetic control, traumatic brain injuries, increased risk behavior likelihood, and parents of youths with anorexia nervosa. A total of 56 outcomes were identified. Two trials provided digital intervention delivery via videoconferencing: one via an interactive graphic interface and one via a web-based program. In total, 23% (14/60) of risk of bias judgments were high risk, 42% (25/60) were some concerns, and 35% (21/60) were low risk. Due to heterogeneity in the data, meta-analysis was deemed inappropriate for 96% (54/56) of outcomes, which were interpreted qualitatively instead. Nonaggregated analyses of mean differences and CIs between delivery modalities yielded mixed results, with superiority of the digital delivery modality for 18% (10/56) of outcomes, superiority of the face-to-face delivery modality for 5% (3/56) of outcomes, equivalence between delivery modalities for 2% (1/56) of outcomes, and neither superiority of one modality nor equivalence between modalities for 75% (42/56) of outcomes. Consequently, for most outcome measures, no indication of superiority or equivalence regarding the relative efficacy of either delivery modality can be made at this stage. We further meta-analytically compared digital versus face-to-face delivery modalities for attrition (risk ratio 1.03, 95% CI 0.52-2.03; P=.93) and number of sessions attended (standardized mean difference –0.11; 95% CI –1.13 to –0.91; P=.83), finding no significant differences between modalities, while CIs falling outside the range of the minimal important difference indicate that equivalence cannot be determined at this stage.
Conclusions: Evidence on digital and face-to-face modalities for systemic psychotherapy interventions is largely heterogeneous, limiting conclusions regarding the differential efficacy of digital and face-to-face delivery. Nonaggregated and meta-analytic analyses did not indicate the superiority of either delivery condition. More research is needed to conclude if digital and face-to-face delivery modalities are generally equivalent or if—and in which contexts—one modality is superior to another.
Investment theory and related theoretical approaches suggest a dynamic interplay between crystallized intelligence, fluid intelligence, and investment traits like need for cognition. Although cross-sectional studies have found positive correlations between these constructs, longitudinal research testing all of their relations over time is scarce. In our pre-registered longitudinal study, we examined whether initial levels of crystallized intelligence, fluid intelligence, and need for cognition predicted changes in each other. We analyzed data from 341 German students in grades 7–9 who were assessed twice, one year apart. Using multi-process latent change score models, we found that changes in fluid intelligence were positively predicted by prior need for cognition, and changes in need for cognition were positively predicted by prior fluid intelligence. Changes in crystallized intelligence were not significantly predicted by prior Gf, prior NFC, or their interaction, contrary to theoretical assumptions. This pattern of results was largely replicated in a model including all constructs simultaneously. Our findings support the notion that intelligence and investment traits, particularly need for cognition, positively interact during cognitive development, but this interplay was unexpectedly limited to Gf.
Attention in social interactions is directed by social cues such as the face or eye region of an interaction partner. Several factors that influence these attentional biases have been identified in the past. However, most findings are based on paradigms with static stimuli and no interaction potential. Therefore, the current study investigated the influence of one of these factors, namely facial affect in natural social interactions using an evaluated eye-tracking setup. In a sample of 35 female participants, we examined how individuals’ gaze behavior responds to changes in the facial affect of an interaction partner trained in affect modulation.
Our goal was to analyze the effects on attention to facial features and to investigate their temporal dynamics in a natural social interaction. The study results, obtained from both aggregated and dynamic analyses, indicate that facial affect has only subtle influences on gaze behavior during social interactions. In a sample with high measurement precision, these findings highlight the difficulties of capturing the subtleties of social attention in more naturalistic settings. The methodology used in this study serves as a foundation for future research on social attention differences in more ecologically valid scenarios.
Job crafting is the behavior that employees engage in to create personally better fitting work environments, for example, by increasing challenging job demands. To better understand the driving forces behind employees’ engagement in job crafting, we investigated implicit and explicit power motives. While implicit motives tend to operate at the unconscious, explicit motives operate at the unconscious level. We focused on power motives, as power is an agentic motive characterized by the need to influence your environment. Although power is relevant to job crafting in its entirety, in this study, we link it to increasing challenging job demands due to its relevance to job control, which falls under the umbrella of power. Using a cross-sectional design, we collected survey data from a sample of Lebanese nurses (N = 360) working in 18 different hospitals across the country. In both implicit and explicit power motive measures, we focused on integrative power that enable people to stay calm and integrate opposition. The results showed that explicit power predicted job crafting (H1) and that implicit power amplified this effect (H2). Furthermore, job crafting mediated the relationship between congruently high power motives and positive work-related outcomes (H3) that were interrelated (H4). Our findings unravel the driving forces behind one of the most important dimensions of job crafting and extend the benefits of motive congruence to work-related outcomes.
In the present study, we tested whether processing information in the context of an ancestral survival scenario enhances episodic memory performance in older adults and in stroke patients. In an online study (Experiment 1), healthy young and older adults rated words according to their relevance to an ancestral survival scenario, and subsequent free recall performance was compared to a pleasantness judgment task and a moving scenario task in a within-subject design. The typical survival processing effect was replicated: Recall rates were highest in the survival task, followed by the moving and the pleasantness judgment task. Although older adults showed overall lower recall rates, there was no evidence for differences between the age groups in the condition effects. Experiment 2 was conducted in a neurological rehabilitation clinic with a sample of patients who had suffered from a stroke within the past 5 months. On the group level, Experiment 2 revealed no significant difference in recall rates between the three conditions. However, when accounting for overall memory abilities and executive function, independently measured in standardized neuropsychological tests, patients showed a significant survival processing effect. Furthermore, only patients with high executive function scores benefitted from the scenario tasks, suggesting that intact executive function may be necessary for a mnemonic benefit. Taken together, our results support the idea that the survival processing task – a well-studied task in the field of experimental psychology – may be incorporated into a strategy to compensate for memory dysfunction.
Older adults who worry about their own cognitive capabilities declining, but who do not show evidence of actual cognitive decline in neuropsychological tests, are at an increased risk of being diagnosed with dementia at a later time. Since neural markers may be more sensitive to early stages of cognitive decline, in the present study we examined whether event-related potential responses
of feedback processing, elicited in a probabilistic learning task, differ between healthy older adults recruited from the community, who either did (subjective cognitive decline/SCD-group) or did not report (No-SCD group) worry about their own cognition declining beyond the normal age-related development. In the absence of group differences in learning from emotionally charged feedback in the probabilistic learning task, the amplitude of the feedback-related negativity (FRN) varied with feedback valence differently in the two groups: In the No-SCD group, the FRN was larger for positive than negative feedback, while in the SCD group, FRN amplitude did not differ between positive and negative feedback. The P3b was enhanced for negative feedback in both groups, and group differences in P3b amplitude were not significant. Altered sensitivity in neural processing of negative versus positive feedback may be a marker of SCD.
Introduction: This study examined the sources and factors of resilience in Russian sexual and gender minorities. We hypothesized that, through their involvement in the lesbian, gay, bisexual, and transgender (LGBT) community (source of resilience), LGBT people establish friendships that provide them with social support (factor of resilience), which in turn should contribute to their mental health.
Method: The study sample consisted of 1,127 young and middle-aged LGBT adults (18 to 50 years) from Russia. We collected the data online and anonymously. Results: Partial mediation could be confirmed. LGBT people who were involved in “their” community reported more social support from friends, which partially mediated the positive association between community involvement and mental health. The mediation remained significant when we controlled for demographics and outness as potential covariates. Additional analyses showed that the present sample reported lower mental health but not less social support than Russian nonminority samples recruited in previous research.
Conclusion: Our study underlines the importance of the LGBT community in times of increasing stigmatization of sexual and gender minorities.