Impact of health care reform on enrolment of immigrants in primary care in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: In 2003, Ontario introduced a patient enrolment system as part of health care reforms, aimed at enhancing primary health care services, but it is unclear whether immigrants have benefited from this health care reform. Therefore, we studied whether this reform changed the extent of immigrants' enrolment in primary care services in Ontario between 2003 and 2012. METHODS: This is a population-based retrospective cohort study, in which a closed cohort of 9231840 Ontario residents between 1985 and 2003 was created, using linked health administrative and immigration databases. Levels of enrolment for traditional and more comprehensive capitation-based practice between 2003 and 2012 were compared by immigrant status. Logistic regression modelling was used to assess the odds of enrolment on primary care practices. RESULTS: Overall enrolment in primary care practices increased gradually after 2004, until 2012, when two-thirds of the cohort (67%) were enrolled. The immigrants' enrolment level remained consistently lower than that of long-term residents over the study period. By 2012, enrolment of immigrants in capitation-based models was significantly lower (17.3% versus 25.4%). In particular, enrolment in Family Health Teams, considered the most comprehensive care model, was considerably lower in immigrants compared with long-term residents (5.6% versus 18.0%; OR = 0.40, 95% CI: 0.40 to 0.41). CONCLUSIONS: Immigrant enrolment rates in new comprehensive primary care models were consistently lower than among long-term residents. This has implication on equitable primary care access for immigrant populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".