Evidence-Informed Oral Health Policy Making: Opportunities and Challenges
Bibliographic record
Abstract
Despite a clear need for improvement in oral health systems, progress in oral health systems transformation has been slow. Substantial gaps persist in leveraging evidence and stakeholder values for collective problem solving. To truly enable evidence-informed oral health policy making, substantial "know-how" and "know-do" gaps still need to be overcome. However, there is a unique opportunity for the oral health community to learn and evolve from previous successes and failures in evidence-informed health policy making. As stated by the Global Commission on Evidence to Address Societal Challenges, COVID-19 has created a once-in-a-generation focus on evidence, which has fast-tracked collaboration among decision makers, researchers, and evidence intermediaries. In addition, this has led to a growing recognition of the need to formalize and strengthen evidence-support systems. This article provides an overview of recent advancements in evidence-informed health policy making, including normative goals and a health systems taxonomy, the role of evidence-support and evidence-implementation systems to improve context-specific decision-making processes, the evolution of learning health systems, and the important role of citizen deliberations. The article also highlights opportunities for evidence-informed policy making to drive change in oral health systems. All in all, strengthening capacities for evidence-informed health policy making is critical to enable and enact improvements in oral health systems.
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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 | 0.094 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".