Research Khandor et al. Access to primary health care among homeless adults in Toronto, Canada: results from the Street Health survey
Bibliographic record
Abstract
Background: Despite experiencing a disproportionate burden of acute and chronic health issues, many homeless people face barriers to primary health care. Most studies on health care access among homeless populations have been conducted in the United States, and relatively few are available from countries such as Canada that have a system of universal health insurance. We investigated access to primary health care among a representative sample of homeless adults in Toronto, Canada. Methods: Homeless adults were recruited from shelter and meal programs in downtown Toronto between November 2006 and February 2007. Cross-sectional data were collected on demographic characteristics, health status, health determinants and access to health care. We used multivariable logistic regression analysis to investigate the association between having a family doctor as the usual source of health care (an indicator of access to primary care) and health status, proof of health insurance, and substance use after adjustment for demographic characteristics. Results: Of the 366 participants included in our study, 156 (43%) reported having a family doctor. After adjustment for potential confounders and covariates, we found that the odds of having a family doctor significantly decreased with every additional year spent homeless in the participant’s lifetime (adjusted odds ratio [OR] 0.91, 95 % confidence interval [CI] 0.86–0.97). Having a family doctor was significantly associated with being lesbian, gay, bisexual or transgendered
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".