Narrative review of affinities and differences between the social determinants of oral and general health in Canada: establishing a common agenda
Bibliographic record
Abstract
BACKGROUND: This article overviews Canadian work on the social determinants of oral and general health noting their affinities and differences. METHODS: A literature search identified Canadian journal articles addressing the social determinants of oral health and/or oral health inequalities. Analysis identified affinities and differences with six themes in the general social determinants of health literature. RESULTS: While most Canadian social determinants activity focuses on physical and mental health there is a growing literature on oral health-literature reviews, empirical studies and policy analyses-with many affinities to the broader literature. In addition, since Canada provides physical and mental health services on a universal basis, but does not do so for dental care, there is a special concern with the reasons behind, and the health effects-oral, physical and mental-of the absence of publicly financed dental care. CONCLUSIONS: The affinities between the social determinants of oral health and the broader social determinants of health literature suggests the value of establishing a common research and action agenda. This would involve collaborative research into common social determinants of oral and general health and combined policy advocacy efforts to improve Canadians' living and working conditions as means of achieving health for all.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".