Access to primary care amongst newcomers in Hamilton, Ontario
Bibliographic record
Abstract
Abstract Background More than one in five Canadians (6.5 million people) do not have a family doctor or nurse practitioner they see regularly. Access gaps are greater among immigrants and marginalized populations, who face systemic, cultural, and language barriers to care. Objectives To evaluate access to primary care provider and dentist among residents of a neighborhood with a high proportion of visible minorities in Hamilton, ON. Methods Between 2022 and 2024, adults living in Riverdale, a neighbourhood in the city of Hamilton, Ontario in which 51% families were born outside the country, were invited to participate in a cross-sectional survey. Determinants of access to these services were identified using multivariable logistic regression modelling. Results 930 people completed the survey. Of these, 48% were not born in Canada. The median age of participants was 39 years, with a median time living in Canada of 28 years. Of those who responded, 79.6% had a primary care provider; and 57.1% had a dentist. In multivariable models, living in Canada < 5 years (OR = 0.10; 95% CI: 0.05, 0.20), male sex (OR= 0.56; 95%CI: 0.38, 0.82) and being unmarried (OR = 0.41; 95% CI: 0.27, 0.64) were associated with lower odds of having a primary care provider. Living in Canada for < 5 years (OR = 0.20; 95% CI: 0.11, 0.35), male sex (OR =0.74; 95% CI: 0.55, 0.99), and employment while living below the poverty line (OR=0.50; 95% CI: 0.29, 0.90) were linked to lower access to dental care. Conclusion In a neighborhood with high proportion of visible minority newcomers in Hamilton, ON, 20% of those surveyed did not have access to a primary care provider, and 43% did not have access to a dentist. Access to primary care was lowest amongst newcomers (within 5 years), men, and those who are unmarried.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".