Barriers to oral care: a cross-sectional analysis of the Canadian longitudinal study on aging (CLSA)
Bibliographic record
Abstract
Abstract Background Oral health plays a role in overall health, indicating the need to identify barriers to accessing oral care. The objective of this study was to identify barriers to accessing oral health care and examine the association between socioeconomic, psychosocial, and physical measures with access to oral health care among older Canadians. Methods A cross-sectional study was conducted using data from the Canadian Longitudinal Study on Aging (CLSA) follow-up 1 survey to analyze dental insurance and last oral health care visit. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between socioeconomic, psychosocial, and physical measures with access to oral care, measured by dental insurance and last oral health visit. Results Among the 44,011 adults included in the study, 40% reported not having dental insurance while 15% had not visited an oral health professional in the previous 12 months. Several factors were identified as barriers to accessing oral health care including, no dental insurance, low household income, rural residence, and having no natural teeth. People with an annual income of <$50,000 were four times more likely to not have dental insurance (adjusted OR: 4.09; 95% CI: 3.80–4.39) and three times more likely to report not visiting an oral health professional in the previous 12 months (adjusted OR: 3.07; 95% CI: 2.74–3.44) compared to those with annual income greater than $100,000. Conclusions Identifying barriers to oral health care is important when developing public health strategies to improve access, however, further research is needed to identify the mechanisms as to why these barriers exist.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".