Identifying effective physical activity guideline dissemination and implementation strategies on health care professionals in a cancer care context: A systematic review
Bibliographic record
Abstract
Newly-developed physical activity guidelines for people with cancer were released to assist health care professionals (HCPs) in providing evidence-based physical activity recommendations to patients. Given the paucity of research examining effective strategies for physical activity guideline dissemination and implementation for HCPs, reviewing the literature for effective strategies for clinical practice guidelines (CPGs), broadly defined, is required. The purpose of this systematic review was to determine the effectiveness of CPG dissemination and implementation strategies among HCPs in the cancer care context. A comprehensive search of five electronic databases was conducted. Studies were limited to the dissemination and/or implementation of a CPG targeting cancer care HCPs. Two reviewers independently assessed study quality and coded intervention strategies using the Mazza taxonomy in the 28 included studies. Interventions used 22 of a possible 49 strategies, with a mean number of 3 strategies per intervention (SD=1.45). Educational strategies (n=19), providing reminders (n=10), and distributing the guideline (n=9) were the most utilized strategies targeting HCP behaviour. Organizational and group education strategies used as part of multi-component interventions were found to correspond with positive changes in HCP behaviour and/or patient outcomes. When used as independent intervention strategies, providing reminders and feedback on compliance corresponded with positive changes in HCP behaviour and/or patient outcomes. Future physical activity guideline dissemination and implementation interventions for cancer care HCPs may benefit from utilizing the above intervention strategies. However, exploring currently unused strategies may identify additional suitable physical activity guideline dissemination and implementation intervention strategies in the cancer care context.Acknowledgments: This study was funded by a Knowledge Translation Canada Research Project Stipend (TGF97068) awarded to JRT.
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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 | 0.028 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".