Government spending on dental care: is it a public priority?
Bibliographic record
Abstract
OBJECTIVES: The majority of Canadians believe that the government should play some role in providing dental care within Canada's health care system. However, it is unclear whether Canadians consider this as a top public priority. This study determines whether dental care is a public priority among Canadian adults relative to other policy concerns and identifies factors predictive of a first priority ranking for dental care. METHODS: Data were collected in 2008 from a national random sample of 1,005 Canadian adults through a telephone interview survey. Respondents were asked to rank five spending priorities (dental care, pharmacare, home care, vision care, and child care) in terms of preferences for new government spending. Simple descriptive analyses were undertaken based on sociodemographic characteristics. Logistic regression modeling was conducted to determine which factors are predictive of a first priority ranking for dental care. RESULTS: Comparatively, dental care stands as the third choice among the other spending priority areas. Approximately 21 percent of adults consider dental care a first priority for spending. First priority ranking of dental care appears to be linked to socioeconomic factors: household income, educational attainment, and dental insurance coverage. CONCLUSIONS: As a public priority, a moderate level of demand exists for more government spending on dental care in Canada, specifically among those of low income, low educational attainment, and who lack dental insurance coverage. A sustained effort should be made to push forward public dental care policies that target priority population subgroups.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".