Transgender and Gender-Diverse People's Experiences of Minority Stress, Mental Health, and Resilience in Relation to Perceptions of Sociopolitical Contexts
Bibliographic record
Abstract
Purpose: The sociopolitical context in which transgender and gender-diverse (TGD) people live has significant effects on mental health. We examined whether perceptions of context (TGD people's perceptions of how TGD people were viewed) differed across four United States (U.S.) states and associations with mental health and identity pride, the mediational effects of minority stressors, and potential buffering effects of resilience. Methods: =158; ages 19-70, mean=33.06) completed questionnaires assessing their perceptions of how TGD people were viewed in their local area and in the U.S., as well as scales assessing minority stressors, pride, resilience, and mental health. Data were collected during Fall 2019 to Spring 2020. Results: Oregon participants viewed perceptions in their state the most positively, with no state-level differences in terms of broader U.S. perceptions. Tennessee participants experienced more expectations of rejection; however, there were no differences across the states in other minority stress variables, identity pride, resilience, or mental health. Participants who viewed their area as having more negative views of TGD people reported higher levels of discrimination, expectations of negative events, internalized stigma, and anxiety, as well as less pride. The effects of perceptions of local context on mental health were partially explained by enacted stigma and internalized stigma. Resilience did not buffer the effects of perceptions of the local context on mental health or pride. Conclusion: Context is important to shaping exposure to minority stressors and mental health, potentially through increasing enacted and internalized stigma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".