Dissemination and implementation of clinical practice guidelines: a longitudinal, mixed-methods evaluation of the Canadian Task Force on Preventive Health Care’s knowledge translation efforts
Bibliographic record
Abstract
BACKGROUND: The Canadian Task Force on Preventive Health Care (task force) develops evidence-based preventive health care guidelines and knowledge translation (KT) tools to facilitate guideline dissemination and implementation. We aimed to determine practitioners' awareness of task force guidelines and KT tools and explore barriers and facilitators to their use. METHODS: The task force's KT team completed annual evaluations using surveys and interviews with primary care providers in Canada from 2014 to 2020, to assess practitioners' awareness and determinants of use of task force guidelines and tools. We transcribed interviews verbatim and double-coded them using a framework analysis approach. RESULTS: A total of 1284 primary care practitioners completed surveys and 183 participated in interviews. On average, 79.9% of participants were aware of the task force's 7 cancer screening guidelines, 36.2% were aware of the other 6 screening guidelines and 18.6% were aware of the 3 lifestyle or prevention guidelines. Participants identified 13 barriers and 7 facilitators to guideline and KT tool implementation; these were consistent over time. Participants identified strategies at the public and patient, provider and health systems levels to improve uptake of guidelines. INTERPRETATION: Canadian primary care practitioners were more aware of task force cancer screening guidelines than its other preventive health guidelines. Over the 6-year period, participants consistently reported barriers to guideline uptake, including misalignment with patient preferences and other provincial or specialty guideline organizations. Further evaluations will assess tailored strategies to address the barriers identified.
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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 | 0.164 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".