Establishing a comprehensive continuum from an evidentiary base to policy development for health technologies: The Ontario experience
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to describe a comprehensive continuum that has developed in Ontario between government and key stakeholder groups, including hospitals, physicians, academic institutions, clinical epidemiologists, health economists, industry, and bioethicists to achieve evidence-based recommendations for policy development. METHODS: The various components of the comprehensive model that has evolved to develop an evidentiary platform for policy development are summarized, and the flow between these components is described. RESULTS: The development of the Ontario Health Technology Advisory Committee (OHTAC) and associated programs demonstrate the need to go beyond the traditional steps taken within most health technology assessment paradigms. These components include pragmatic postmarketing studies, human factors, and safety analyses, and formalized interactions with a broad spectrum of potential end-users of each technology, experts, and industry. Thesecomponents, taken together with an expanded systematic review to include a range of economic analyses, and societal impacts augment the traditional systematic review processes. This approach has been found to be important in assisting decision making and has resulted in an 81 percent conversion from evidence to policy consideration for eighty-three technologies that had been assessed at the time this article was submitted. CONCLUSIONS: The comprehensive model, centered around OHTAC, has added important new dimensions to health policy by improving its relevance to decision makers and providing an accountable and transparent basis for government to invest appropriately in health technologies. This study could also form a basis for further research into appropriate methodologies and outcome measurements as they relate to each component of this approach.
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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 | 0.124 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| 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".