Self‐reported cost‐prohibitive dental care needs among Canadians
Bibliographic record
Abstract
OBJECTIVES: To explore self-reported cost-prohibitive dental treatment needs among Canadians. METHODS: Data were collected through a national telephone interview survey of 1006 randomly selected Canadian adults. Descriptive analyses based on socio-demographic characteristics and dental-related behaviours were undertaken. Logistic regression was used to determine the predictors of experiencing a cost-prohibitive dental care need. Chi-square tests were used to determine significant differences in the treatments reported as unaffordable by socio-demographic characteristics and dental-related behaviours. RESULTS: Those of low income, no insurance coverage and poor self-rated oral health were more likely to report having a cost-prohibitive dental care need. The top needs reported as unaffordable were fillings, cleanings and check-ups. Comparatively, preventive services were selected as cost-prohibitive more often by the insured, dentures by the oldest group and extractions by those with a high school education or less. CONCLUSIONS: This study confirms that there are significant relationships between socio-demographic factors, dental-related behaviours and the types of dental services that are selected as unaffordable. Indirectly, this shows us how socio-demographic factors may influence the types of dental services that are reported as 'needed' by certain groups. Difficulties in distinguishing between the services that are 'needed' from and those that are 'wanted' demonstrate some of the policy complexity associated with publicly financed dental care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".