Sexual and Gender Minority Youth in ACCESS Open Minds: Severity at Presentation to Diverse Youth Mental Health Services
Bibliographic record
Abstract
Childhood and adolescence are crucial periods in the development of healthy individuals. They are periods of significantly increased vulnerability to developing mental illness. Among youth, sexual and gender minorities face greater adversity and higher mental health risks than their cisgender and heterosexual peers, with gender minorities experiencing worse outcomes than sexual minorities. While substantial research has examined adversities and mental health disparities for sexual and gender minority youth relative to the general population, no study has investigated how severity at presentation to healthcare services differs between sexual and gender minorities. Since gender minorities face more adversity, it was hypothesized that their severity at the initial assessment would be higher. Using the ACCESS Open Minds database, which includes data from 5,232 youth aged 11 to 25 who were referred for or sought mental health help at 12 diverse sites across Canada, the severity of mental health presentations of 722 sexual minority youth and 258 gender minority youth was analyzed. Severity was determined by scores on the Kessler Psychological Distress Scale (K10), Clinical Global Impression (CGI) scale, and the Social and Occupational Functioning Assessment Scale (SOFAS). No significant differences were found for K10 and CGI, but gender minority youth had significantly lower SOFAS scores than sexual minority youth. Differences in social and occupational functioning without differences in severity of mental distress may reflect an important role for social discrimination against gender minorities and its consequent impact on the social and vocational possibilities for these youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".