Identification of knowledge translation theories, models or frameworks suitable for health technology reassessment: a survey of international experts
Bibliographic record
Abstract
OBJECTIVE: Health technology reassessment (HTR) is a field focused on managing a technology throughout its life cycle for optimal use. The process results in one of four possible recommendations: increase use, decrease use, no change or complete withdrawal of the technology. However, implementation of these recommendations has been challenging. This paper explores knowledge translation (KT) theories, models and frameworks (TMFs) and their suitability for implementation of HTR recommendations. DESIGN: Cross-sectional survey. PARTICIPANTS: Purposeful sampling of international KT and HTR experts was administered between January and March 2019. METHODS: Sixteen full-spectrum KT TMFs were rated by the experts as 'yes', 'partially yes' or 'no' on six criteria: familiarity, logical consistency/plausibility, degree of specificity, accessibility, ease of use and HTR suitability. Consensus was determined as a rating of ≥70% responding 'yes'. Descriptive statistics and manifest content analysis were conducted on open-ended comments. RESULTS: Eleven HTR and 11 KT experts from Canada, USA, UK, Australia, Germany, Spain, Italy and Sweden participated. Of the 16 KT TMFs, none received ≥70% rating. When ratings of 'yes' and 'partially yes' were combined, the Consolidated Framework for Implementation Research was considered the most suitable KT TMF by both KT and HTR experts (86%). One additional KT TMF was selected by KT experts: Knowledge to Action framework. HTR experts selected two additional KT TMFs: Co-KT framework and Plan-Do-Study-Act cycle. Experts identified three key characteristics of a KT TMF that may be important to consider: practicality, guidance on implementation and KT TMF adaptability. CONCLUSIONS: Despite not reaching an overall ≥70% rating on any of the KT TMFs, experts identified four KT TMFs suitable for HTR. Users may apply these KT TMFs in the implementation of HTR recommendations. In addition, KT TMF characteristics relevant to the field of HTR need to be explored further.
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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 | 0.115 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".