Daily experiences of minority stress and mental health in transgender and gender-diverse individuals.
Bibliographic record
Abstract
age = 25) retained in the daily surveys. Participants completed surveys for 56 days reflecting exposure to marginalization, gender nonaffirmation, internalized stigma, rumination, isolation, affect (negative, anxious, and positive affect), and mental health (anxiety and depression symptoms). Participants experienced marginalization on 25.1% of the days. Within-person analyses revealed concurrent associations between marginalization and gender nonaffirmation with increased negative and anxious affect and increased anxiety and depression symptoms, as well as associations for gender nonaffirmation and decreased positive affect. At the within-person level, there were prospective associations between marginalization and gender nonaffirmation with increased negative affect on the next day, as well as increased anxiety and depression symptoms the next week. Concurrent analyses revealed significant indirect effects with marginalization and gender nonaffirmation associated with all three affect variables and mental health via increases in internalized stigma, rumination, and isolation. However, only gender nonaffirmation was related to isolation and affect or mental health in the prospective analyses. Clinical considerations include strategies to address the immediate effects of minority stress as well as the long-term interpersonal effects. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".