Immigration as a Social Determinant of Oral Health: Does the 'Healthy Immigrant Effect' Extend to Self-rated Oral Health in Ontario, Canada?
Bibliographic record
Abstract
Immigrants' oral health is rarely explored in Canada, despite its impact on physical, social, and economic well-being. Drawing on data from the 2014 Canadian Community Health Survey, we address this void by comparing self-rated oral health among recent immigrants, established immigrants, and the native-born in Ontario, Canada. The analysis includes three sets of control variables capturing the resettlement stress perspective, convergence perspective, and selective immigration. We find that oral health does not significantly differ between recent immigrants and the native-born at the bivariate level, although the odds of reporting poor oral health become significantly lower for recent immigrants, once the resettlement characteristics are accounted for. This difference is completely attenuated by selective immigration characteristics. Established immigrants are more likely to report poor oral health than the native-born, even after accounting for all control variables. Based on these findings, we conclude by offering recommendations for policymakers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".