Characteristics of Knowledge Translation Theories, Models and Frameworks for Health Technology Reassessment: Expert Perspectives Through a Qualitative Exploration
Bibliographic record
Abstract
Abstract Background: Health Technology Reassessment (HTR) is a process that systematically assesses technologies that are currently used in the health care system. The process results in four outputs: increase use or decrease use, no change, or de-adoption of a technology. Implementation of these outputs remains a challenge. The Knowledge Translation (KT) field enables knowledge into practice. KT could help with implementation of HTR outputs. This study sought to identify which characteristics of KT theories, models, and frameworks (TMFs) could be useful, specifically for decrease use or de-adoption of a technology.Methods: A qualitative descriptive approach was used to ascertain the perspectives of international KT and HTR experts on the characteristics of KT TMFs for decrease use or de-adoption of a technology. One-to-one semi-structured interviews were conducted from September to December 2019. Interviews were audio recorded and transcribed verbatim. Themes and sub-themes were deduced from the data through framework analysis using five distinctive steps: familiarization, identifying an analytic framework, indexing, charting, mapping and interpretation. Themes and sub-themes were also mapped to existing KT TMFs.Results: Thirteen individuals from Canada, United States, United Kingdom, Australia, Germany, Spain, and Sweden participated in the study. Three themes emerged that illustrated the ideal traits of a KT TMF: principles that were foundational for HTR, levers of change, and steps for knowledge to action. Principles included evidence-based, high usability, patient-centered, and ability to apply to the micro, meso, macro levels. Levers of change were characterized as positive, neutral, or negative influences for changing behaviour for HTR. Steps for knowledge to action included: build the case for HTR, adapt research knowledge, assess context, select, tailor and implement interventions, and assess impact. Of the KT TMFs that were mapped, the Consolidated Framework for Implementation Research had most of the characteristics, except ability to apply to micro, meso, macro levels. Conclusions: Application of KT TMFs to HTR has not been clearly understood. Characteristics that need to be considered within a KT TMF for implementing HTR outputs have been identified. Consideration of these characteristics within KT TMFs may guide users undertaking HTR projects.
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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 | 0.156 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| 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".