Health care availability, quality, and unmet need: a comparison of transgender and cisgender residents of Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that transgender (trans) individuals in Canada are a medically underserved population; barriers range from lack of provider knowledge on trans issues to refusal of care. This paper provides the first formal estimation of health care inequalities between trans and cisgender individuals in Ontario, Canada. METHODS: Weighted statistics from the Ontario-wide Trans PULSE Project (n = 433) were compared with age-standardized Ontario data from the Canadian Community Health Survey (n = 39,980) to produce standardized prevalence differences (SPDs). Analysis was also conducted separately for trans men and trans women, each compared to the age-standardized Ontario population. RESULTS: An estimated 33.2% (26.4,40.9) of trans Ontarians reported a past-year unmet health care need in excess of the 10.7% expected based on the age-standardized Ontario population. Inequality was greatest comparing trans with cisgender men (SPD = 34.4% (23.0, 46.1). While trans Ontarians evaluated health care availability in Ontario similarly to the broader population, they were significantly more likely to evaluate availability in their community as fair or poor. CONCLUSIONS: Trans Ontarians experience inequalities in perception and reported experiences of health care access, with 43.9% reporting a past-year unmet health care need.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".