FROM TALK TO ACTION: POLICY STAKEHOLDERS, APPROPRIATENESS, AND SELECTIVE DISINVESTMENT
Bibliographic record
Abstract
OBJECTIVES: There is widespread commitment--at least in principle--to "evidence-informed" clinical practice and policy development in health care. The intention is that only "appropriate" care ought to be delivered at public expense. Although the rationale for an appropriateness agenda is widely endorsed, and methods have been proposed for addressing it, few published studies exist of contemporary policy initiatives which have actually led to successful disinvestment. Our objective was to explore whether the direct involvement of policy stakeholders could advance appropriateness and disinvestment. METHODS: Several collaborative engagements with policy stakeholders were undertaken to adapt and combine conceptual and empirical material related to appropriateness and disinvestment from the literature to create tools and processes for use in Canada and the province of Ontario in particular. RESULTS: By combining inputs from the literature with colloquial evidence from policy stakeholders, a definition of appropriateness was developed and, importantly, endorsed by all the provincial and territorial ministers of health in Canada. Second, a reassessment framework was successfully implemented for identifying priorities for selective disinvestment. CONCLUSIONS: When scientific evidence was combined with colloquial evidence from policy stakeholders, progress was made on the design and successful implementation of policies for appropriateness and disinvestment.
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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 | 0.076 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| 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".