Factors associated with unmet dental care needs in Canadian immigrants: an analysis of the longitudinal survey of immigrants to Canada
Bibliographic record
Abstract
BACKGROUND: Immigrants are often considered to have poorer oral health than native born-populations. One possible explanation for immigrants' poor oral health is lack of access to dental care. There is very little information on Canadian immigrants' access to dental care, and unmet dental care needs. This study examines predictors of unmet dental care needs among a sample of adult immigrants to Canada over a three-point-five-year post-migration period. METHODS: A secondary data analysis was conducted on the Longitudinal Survey of Immigrants to Canada (LSIC). Sampling and bootstrap weights were applied to make the data nationally representative. Simple descriptive analyses were conducted to describe the demographic characteristics of the sample. Bivariate and multiple logistic regression analyses were applied to identify factors associated with immigrants' unmet dental care needs over a three-point-five-year period. RESULTS: Approximately 32% of immigrants reported unmet dental care needs. Immigrants lacking dental insurance (OR = 2.63; 95% CI: 2.05-3.37), and those with an average household income of $20,000 to $40,000 per year (OR = 1.62; 95% CI: 1.01-2.61), and lower than $20,000 (OR = 2.25; 95% CI: 1.31-3.86), were more likely to report unmet dental care needs than those earning more than $60,000 per year. In addition, South Asian (OR = 1.85; CI: 1.25-2.73) and Chinese (OR = 2.17; CI: 1.47-3.21) immigrants had significantly higher odds of reporting unmet dental care needs than Europeans. CONCLUSIONS: Lack of dental insurance, low income and ethnicity predicted unmet dental care needs over a three-point-five-year period in a sample of immigrants to 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 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".