Characteristics of Youth Presenting for Gender Care Compared to Background Populations: Examination of Social Determinants of Health
Bibliographic record
Abstract
Purpose: Transgender and gender diverse (TGD) youth in North American clinireports are predominantly White with relatively high socioeconomic status suggesting that access to gender-affirming care is inequitable. This study examined whether socioeconomic and social determinant of health discrepancies exist between a clinical population of TGD youth and surrounding communities. Methods: Patient postal codes were used to link the Ontario Marginalization Index (ON-MARG) to a clinic-based TGD youth cohort ( n = 298). Using ON-MARG, each patient was assigned a quintile score from 1 (least marginalized) to 5 (most marginalized) on four marginalization measures. Mean quintile scores were compared to background populations. Census-based Toronto neighborhood-level data on ethnic diversity and educational status were also examined. Neighborhoods were categorized as highly represented, less represented, or unrepresented based on representation in the clinic cohort. One-way analysis of covariance was used to determine associations between neighborhood-level variables and the degree of neighborhood representation. Results: ON-MARG data demonstrated that clinic patients hailed from areas with more individuals having paid employment. Patients from Toronto and surrounding areas came, in general, from communities with fewer recent immigrants and visible minorities. Highly represented Toronto neighborhoods had smaller proportions of visible minorities and immigrants compared with less and unrepresented neighborhoods. Educational status, represented by adults with bachelor’s degrees, was lower among unrepresented neighborhoods. Conclusion : TGD youth seen in clinic, particularly those from Toronto, are disproportionally White and socioeconomically advantaged. Further research is needed to better understand the underrepresentation of racialized and low-socioeconomic status youth and to inform strategies to improve access to care.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".