Dental utilization among aging immigrants in Canada
Bibliographic record
Abstract
Objective: To compare the dental-care utilization of elderly immigrants to that of non-immigrants in Canada. Materials and Methods: This is a secondary data analysis of publicly available data from the 2008/09 Canadian Community Health Survey: Healthy Aging (CCHS-HA) component. The target population consisted of 30,865 people aged 45 years and above. A modified Andersen’s health-service utilization model was used as the framework for analysis, grouping predisposing (age, sex, marital status, immigrant status, time since immigration, smoking, alcohol use), enabling (Education, household income, dental insurance, social support), need (Self-reported health and self-reported oral health) and behavioural factors (Brushing and physician visit), to compare the dental care utilization between elderly immigrants and non-immigrants. Descriptive statistics and binary and multivariable logistic regressions were performed. Results: Results for the entire population indicate age, sex, marital status, level of education and income, dental insurance, physician visit, smoking and self-reported oral health as significant predictors of dental visits. Predictors for utilization among immigrant seniors were: age, sex, marital status, social interaction, and level of education. Predictors for non-immigrant seniors included: age, level of education, household income, dental insurance, smoking, and self-reported oral health. Conclusion: By comparing elderly immigrants and non-immigrants, this study draws attention to what influences dental care utilization in each group. Implications for oral health policy include integrating oral health insurance into Canada’s universal healthcare system, changes in legislation that improve the availability and access to dental insurance, and better utilization of existing dental public-health resources by including targeted services to elderly 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".