The deterioration of <scp>C</scp>anadian immigrants’ oral health: analysis of the <scp>L</scp>ongitudinal <scp>S</scp>urvey of <scp>I</scp>mmigrants to <scp>C</scp>anada
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of immigration on the self-reported oral health of immigrants to Canada over a 4-year period. METHODS: The study used Statistics Canada's Longitudinal Survey of Immigrants to Canada (LSIC 2001-2005). The target population comprised 3976 non-refugee immigrants to Canada. The dependent variable was self-reported dental problems. The independent variables were as follows: age, sex, ethnicity, income, education, perceived discrimination, history of social assistance, social support, and official language proficiency. A generalized estimation equation approach was used to assess the association between dependent and independent variables. RESULTS: After 2 years, the proportion of immigrants reporting dental problems more than tripled (32.6%) and remained approximately the same at 4 years after immigrating (33.3%). Over time, immigrants were more likely to report dental problems (OR = 2.77; 95% CI 2.55-3.02). An increase in self-reported dental problems over time was associated with sex, history of social assistance, total household income, and self-perceived discrimination. CONCLUSION: An increased likelihood of reporting dental problems occurred over time. Immigrants should arguably constitute an important focus of public policy and programmes aimed at improving their oral health and access to dental care in Canada.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".