Mental health challenges and resilience in lesbian, gay, and bisexual young adults : biological and psychological internalization of minority stress and victimization
Bibliographic record
Abstract
This study was a novel exploration of psycho-social and neuroendocrine factors that may contribute to mental health in LGB youth and young adults. Research suggests that, compared to their heterosexual peers, LGB youth and young adults report alarmingly elevated rates of depression, anxiety, suicidal ideation and attempts, and lower self-esteem in concert with a much higher incidence of victimization from family and peer-groups. Homophobic societal attitudes compounded with discrimination, marginalization, stigma, and victimization experienced by LGB individuals are thought to contribute to the development of internalized homonegativity (IH) and psychological distress. Research also suggests that victimization and social stressors can dysregulate cortisol activity, leading to increased risk for mood disorders. The main goal of the present study was to investigate the interplay of bio-psycho-social factors that are thought to contribute to positive and negative indices of mental health in a community sample of 63 LGB young adults in Montreal, Canada. Dysregulated diurnal cortisol activity and internalized homonegativity were postulated to function either as mediators or as moderators of the links between parental support, social support and LGB-related stress on the one hand, and depression, anxiety, suicidality and self esteem on the other. LGB young adults experiencing more LGB-related stress had more depressive symptoms and lower self-esteem. Interaction effects with parental and social support were also found. IH and cortisol were both positively correlated with LGB-related stress. IH and cortisol slope jointly predicted depression, but each uniquely predicted the other measures of mental health. LGB young adults experiencing varying levels of IH benefited from different social relationships. Cortisol slope also moderated the relations between support, victimization and mental health. These results are particularly striking given the overall high-level of mental-health of this particular sample. The findings of this study were in accord with diathesis-stress models of mental health. Implications for understanding the bio-psycho-social basis of mental health and promoting the well-being of LGB young adults were considered, as were limitations imposed by the sample and methodology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".