<i>“I Don’t Feel at Home in This World”</i> Sexual and Gender Minority Emerging Adults’ Self-Perceived Links Between Their Suicidal Thoughts and Sexual Orientation or Gender Identity
Bibliographic record
Abstract
OBJECTIVES: To examine whether sexual and gender minority (SGM) emerging adults perceived their SGM status was linked to suicidal ideation, and to explore if their responses fell within tenets of the minority stress framework. METHOD: = 187) were thematically analysed using the constant comparative comparison method for qualitative analysis. RESULTS: We identified 8 themes in our qualitative analysis. Two themes fell within the scope of the minority stress framework that has received little attention: (1) concerns about relationships and family planning and (2) feeling different (internal stressor). Two additional themes emerged largely beyond the scope of existing minority stress framework studies on suicidality: (3) SGM-related questioning; (4) negativity in LGBT communities. Four established minority stress framework themes emerged: (5) gender identity stress; (6) victimization; (7) coming-out stress; (8) psychological difficulties linked to SGM status. CONCLUSION: Suicide prevention needs to focus on supporting SGM emerging adults who worry about feeling "different", or who have concerns over their romantic and family life, on reducing gender minority stress, as well as on caring for those who are victimized due to their sexual or gender identity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".