Transnational dental care among Canadian immigrants
Bibliographic record
Abstract
OBJECTIVE: This study examines predictors of transnational dental care utilization, or the use of dental care across national borders, over a 4-year period among immigrants to Canada. METHODS: Data from the Longitudinal Survey of Immigrants to Canada (LSIC, 2001-2005) were used. Sampling and bootstrap weights were applied to make the data nationally representative. Bivariate and multiple logistic regression analyses were applied to identify factors associated with immigrants' transnational dental care utilization. RESULTS: Approximately 13% of immigrants received dental care outside Canada over a period of 4 years. Immigrants lacking dental insurance (OR = 2.05; 95% CI: 1.55-2.70), those reporting dental problems (OR = 1.45; 95% CI: 1.12-1.88), who were female (OR = 1.59; 95% CI: 1.22-2.08), aged ≥ 50 years (OR = 2.30; 95% CI: 1.45-3.64), and who were always unemployed (OR = 1.70; 95% CI: 1.20-2.39) were more likely to report transnational dental care utilization. History of social assistance was inversely correlated with the use of dental services outside Canada (OR = 0.48; 95% CI: 0.30-0.83). CONCLUSIONS: It is estimated that roughly 11 500 immigrants have used dental care outside Canada over a 4-year period. Although transnational dental care utilization may serve as an individual solution for immigrants' initial barriers to accessing dental care, it demonstrates weaknesses to in-country efforts at providing publicly funded dental care to socially marginalized groups. Policy reforms should be enacted to expand dental care coverage among adult immigrants.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".