Trends in self-reported cost barriers to dental care in Ontario
Bibliographic record
Abstract
BACKGROUND: The affordability of dental care continues to receive attention in Canada. Since most dental care is privately financed, the use of dental care is largely influenced by insurance coverage and the ability to pay-out-of pocket. OBJECTIVES: i) to explore trends in self-reported cost barriers to dental care in Ontario; ii) to assess trends in the socio-demographic characteristics of Ontarians reporting cost barriers to dental care; and iii) to identify the trend in what attributes predicts reporting cost barriers to dental care in Ontario. METHODS: A secondary data analysis of five cycles (2003, 2005, 2009-10, 2013-14 and 2017-18) of the Canadian Community Health Survey (CCHS) was undertaken. The CCHS is a cross-sectional survey that collects information related to health status, health care utilization, and health determinants for the Canadian population. Univariate and bivariate analyses were conducted to determine the characteristics of Ontarians who reported cost barriers to dental care. Poisson regression was used to calculate unadjusted and adjusted prevalence ratios to determine the predictors of reporting a cost barrier to dental care. RESULTS: In 2014, 34% of Ontarians avoided visiting a dental professional in the past three years due to cost, up from 22% in 2003. Having no insurance was the strongest predictor for reporting cost barriers to dental care, followed by being 20-39 years of age and having a lower income. CONCLUSION: Self-reported cost barriers to dental care have generally increased in Ontario but more so for those with no insurance, low income, and aged 20-39 years.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".