Effectiveness of guideline dissemination and implementation strategies on health care professionals’ behaviour and patient outcomes in the cancer care context: a systematic review
Bibliographic record
Abstract
BACKGROUND: Health care professionals (HCPs) use clinical practice guidelines (CPGs) to make evidence-informed decisions regarding patient care. Although a large number of cancer-related CPGs exist, it is unknown which CPG dissemination and implementation strategies are effective for improving HCP behaviour and patient outcomes in a cancer care context. This review aimed to determine the effectiveness of CPG dissemination and/or implementation strategies among HCPs in a cancer care context. METHODS: A comprehensive search of five electronic databases was conducted. Studies were limited to the dissemination and/or implementation of a CPG targeting both medical and/or allied HCPs in cancer care. Two reviewers independently coded strategies using the Mazza taxonomy, extracted study findings, and assessed study quality. RESULTS: The search strategy identified 33 studies targeting medical and/or allied HCPs. Across the 33 studies, 23 of a possible 49 strategies in the Mazza taxonomy were used, with a mean number of 3.25 (SD = 1.45) strategies per intervention. The number of strategies used per intervention was not associated with positive outcomes. Educational strategies (n = 24), feedback on guideline compliance (n = 11), and providing reminders (n = 10) were the most utilized strategies. When used independently, providing reminders and feedback on CPG compliance corresponded with positive significant changes in outcomes. Further, when used as part of multi-strategy interventions, group education and organizational strategies (e.g. creation of an implementation team) corresponded with positive significant changes in outcomes. CONCLUSIONS: Future CPG dissemination and implementation interventions for cancer care HCPs may benefit from utilizing the identified strategies. Research in this area should aim for better alignment between study objectives, intervention design, and evaluation measures, and should seek to incorporate theory in intervention design, so that behavioural antecedents are considered and measured; doing so would enhance the field's understanding of the causal mechanisms by which interventions lead, or do not lead, to changes in outcomes at all levels.
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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 | 0.023 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".