Socio-demographic predictors of not having private dental insurance coverage: machine-learning algorithms may help identify the disadvantaged
Bibliographic record
Abstract
Abstract Background For accessing dental care in Canada, approximately 62% of the population has employment-based insurance, 6% have some publicly funded coverage, and 32% have to pay out-of pocket. Those with no insurance or public coverage find dental care more unaffordable compared to those with private insurance. To support the development of more comprehensive publicly funded dental care programs, it is important to understand the socio-demographic attributes of all those, who find dental care unaffordable. Methods This study is a secondary analysis of the data collected from Ontarians during the latest available cycle of the Canadian Community Health Survey (2017-18), a cross-sectional survey that collects information on health status, health care utilization, and health determinants for the Canadian population. First, bivariate analysis was conducted to determine the characteristics of Ontarians who lack dental insurance. Afterwards, we employed machine learning (ML) to analyze data and identify risk indicators for not having private dental insurance. Specifically, we trained several supervised ML models and utilized Shapley additive explanations (SHAP) to determine the relative feature importance for not having private dental insurance from the best ML model [the gradient boosting (GBM)]. Results Approximately one-third of Ontarians do not have private insurance coverage for dental care. Individuals with an income below $20,000, those unemployed or working part-time, seniors aged above 70, and those unable to afford to have their own housing are more at risk of not having private dental insurance, leading to financial barriers in accessing dental care. Conclusion In the future, government-funded programs can incorporate these identified risk indicators when determining eligible populations for publicly funded dental programs. Understanding these attributes is critical for developing targeted and effective interventions, ensuring equitable access to dental care for Canadians.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".