Understanding the Relationship Between Health Technology Reassessment and Knowledge Translation
Bibliographic record
Abstract
Until now, it was not well understood how the field of Knowledge Translation (KT) would be applicable to Health Technology Reassessment (HTR). This thesis reports on three studies to determine how KT approaches are used to translate HTR outputs to achieve the desired outcomes. The first study was a scoping review of full-spectrum (phases of planning/design, evaluation, implementation, sustainability/scalability) KT Theories, Models, Frameworks (KT TMFs). Thirty-six KT TMFs were identified and categorized according to five approaches: process models, determinant frameworks, classic theories, implementation theories, and evaluation frameworks. It provided a starting point for the selection of KT TMFs for HTR. The second study employed a modified Delphi process and expert survey to review the 36 full-spectrum KT TMFs and determined which may be suitable for HTR. The three-round modified Delphi process resulted in 16 KT TMFs. Twenty-two international experts (11 KT and 11 HTR) were surveyed. None of the 16 KT TMFs reached ≥ 70% agreement when ratings of “yes” were considered. However when ratings of “yes” and “partially yes” were combined, the Consolidated Framework for Implementation Research (CFIR) was considered the most suitable by both KT and HTR experts (86%). One additional KT TMF was selected by KT experts: the Knowledge-to-Action framework. HTR experts selected two additional KT TMFs: the co-KT framework and the Plan-Do-Study-Act cycle. The third study involved 13 one-to-one semi-structured interviews on characteristics of KT TMFs that were important to consider for the HTR outputs of decreased use or de-adoption of a technology. Four foundational principles, three levers of change, and five steps for knowledge to action emerged as KT TMF traits for HTR. From the KT TMFs that were mapped onto the characteristics, CFIR had the most characteristics (11/12) missing only the ability to map to the micro, meso, macro levels. This is the first body of work that examines the relationship between HTR and KT. The findings offer guidance to users on the application of KT TMFs to the HTR process and implementation of its outputs. Practical use of these KT TMFs to the HTR process will provide further advancement in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".