Supporting the use of health technology assessments in policy making about health systems
Bibliographic record
Abstract
OBJECTIVES: The objective of this study is to profile the health technology assessments (HTAs) produced in Canada and other selected countries and assess their potential to inform policy making about health systems in jurisdictions other than the ones for which they were produced, and to develop and pilot test prototypes for packaging and assessing the relevance of HTAs for health system managers and policy makers. METHODS: We compiled an inventory of all HTAs that were produced by nine HTA agencies between September 2003 and August 2006; coded the title and abstract of each HTA according to the technologies assessed, methods used, and whether or not context-specific actionable messages were provided; developed a prototype for a structured, decision-relevant HTA summary and for a relevance-assessment form; and pilot-tested the prototypes using semistructured telephone interviews with a purposive sample of Canadian healthcare managers and policy makers. RESULTS: Our review of the 223 HTAs identified that: (i) 44 HTAs addressed health system arrangements (20 percent); (ii) 205 incorporated a systematic review (92 percent), whereas only 12 incorporated a sociopolitical assessment using explicit methods (5 percent); and (iii) 50 contained context-specific actionable messages (22 percent). Our interviews identified significant support for both the general idea of an HTA summary and the prototype's specific elements, but mixed views about using peer assessments of relevance. CONCLUSIONS: Those involved in supporting the use of HTAs in policy making about health systems may wish to produce structured decision-relevant summaries for their systematic review-containing HTAs to increase the prospects for their HTAs being used outside the jurisdiction for which they were produced.
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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 | 0.367 | 0.494 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".