Using health technology assessment to support evidence-based decision-making in Canada: an academic perspective
Bibliographic record
Abstract
Health technology assessment (HTA) adopts a multidisciplinary approach to comprehensively assess safety, efficacy, effectiveness, economic and organizational impact, and any potential social and ethical implications associated with adoption and diffusion of a technology in a healthcare system. Canada was one of the first pioneers in using HTA as a research tool to support evidence-based decision-making. This article describes the current application of HTA in Canada, with a focus on some federal and Ontario initiatives in which the authors have extensive knowledge and experience to illustrate how academic researchers conduct HTA in collaboration with decision makers. Some issues and challenges are also highlighted that will hopefully stimulate a broader discussion among HTA stakeholders to move HTA forward.
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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 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.025 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".