Factors associated with primary healthcare provider access among trans and non-binary immigrants, refugees, and newcomers in Canada
Bibliographic record
Abstract
Objective: Trans and non-binary (TNB) immigrants, refugees, and newcomers (IRN) face intersecting challenges and barriers, including stigma and persecution in countries of origin, and others unique to the Canadian resettlement process. The present study aimed to investigate factors that are associated with having a primary healthcare provider among TNB IRN. Design: Trans PULSE Canada was a community-based, national study of health and wellbeing among 2,873 TNB people residing in Canada, aged 14 and older, who were recruited using a multi-mode convenience sampling approach.. The survey asked questions about identity, community, service access, health - and IRN were asked questions specific to immigration/settlement. Results: =0.75), 76.4 % had a primary healthcare provider. TNB IRN largely reported being Canadian citizens (59.8 %), gender non-binary or similar (46.9 %), currently living in Ontario (35.5 %), and having immigrated from the United States (32.1 %). Chi-square analyses revealed that having a primary healthcare provider was associated with age, gender identity, citizenship status, region of origin, current location in Canada, length of time since immigrating to Canada, status in gender affirming medical care, and having extended health insurance. With modified Poisson regression, we found that TNB IRN who were non-permanent residents, originating from European, African, and Oceania regions, or living in Quebec and the Prairie provinces were less likely to have a primary healthcare provider. Conclusion: Results may inform settlement organizations of the unique needs and barriers of TNB IRN. Schools and LGBTQ+ organizations may better serve this population - especially those originating from highlighted regions, who live in Quebec or the Prairie provinces, and/or are non-permanent residents - by offering programs that connect them to primary healthcare providers who are competent in cross-cultural trans health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".