Avoidance of primary healthcare among transgender and non-binary people in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
Transgender (trans) and non-binary people experience barriers to culturally competent healthcare and many have reported avoiding care. COVID-19 and related mitigation strategies may have exacerbated avoidance, and poor mental health may be bidirectionally related to avoiding care. This study estimated the prevalence of primary care avoidance during the pandemic in a national sample of trans and non-binary people in Canada with a primary care provider and examined the association between poorer self-rated mental health and avoidance. In Fall 2019, Trans PULSE Canada collected multi-mode survey data from trans and non-binary people. In September to October 2020, 820 participants completed a COVID-19-focused survey. In this cross-sectional analysis, multivariable logistic regression models estimated odds ratios adjusted for confounders and weighted to the 2019 sample. The analysis included 689 individuals with a primary healthcare provider, of whom 61.2% (95% CI: 57.2, 65.2) reported fair or poor mental health and 25.7% (95% CI: 22.3, 29.2) reported care avoidance during the pandemic. The most common reason for avoidance was having a non-urgent health concern (72.7%, 95% CI: 65.9, 79.5). In adjusted analyses, those with fair or poor mental health had higher odds of avoiding primary care as compared to those with good to excellent mental health (adjusted odds ratio [AOR] = 2.37; 95% CI: 1.50, 3.77). This relationship was similar when excluding COVID-related reasons for avoidance (AOR = 2.52; 95% CI: 1.52, 4.17). Expansion of virtual communication may enhance primary care accessibility, and proactively assessing mental health symptoms may facilitate connections to gender-affirming mental health services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".