Oral health and its determinants among elderly immigrant Canadians
Bibliographic record
Abstract
Objective: To compare the self-reported oral health status of elderly immigrant to Canadian-born (non-immigrant) seniors in Canada. Materials and Methods: This was a secondary data analysis of publicly available data from the 2008/09 Canadian Community Health Survey: Healthy Aging component (CCHS-HA). The sample consisted of 30,865 people aged 45 years and above. The outcome was self-reported oral health. We used 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 behavioural variables (brushing, physician and dentist visits) to compare the oral health status between immigrant and non-immigrant elders. Descriptive statistics and binary logistic regression were performed. Results: 16.9% of elderly immigrants reported fair to poor self-rated oral health compared to 10.5% non-immigrants. The predictors influencing fair to poor oral health among immigrant and non-immigrant elders varied. Significant predictors for immigrant elders included age, gender, marital status, education, income and physician visits. For non-immigrant seniors, last dental visit, income and education played a role in how oral health status was reported. Conclusion: An increasing influx of immigrants-coupled with an increasingly aging population, including elderly immigrants-has important public health policy implications. Policy approaches that incorporate oral health education, dental screening, awareness raising, and community-based initiatives in immigrant-concentrated areas would be beneficial when targeting the low-socio-economic status elderly immigrant population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".