Practice Guideline Dissemination and Implementation Strategies for Healthcare Teams and Team-Based Practice: a systematic review
Bibliographic record
Abstract
Objectives The objective of this systematic review is to describe and identify the effectiveness of different practice guideline implementation strategies on team-based practice and/or patient outcomes. Methods A systematic review was conducted, using a comprehensive, reproducible search strategy that revealed 88 studies that met the inclusion criteria. Results A descriptive analysis revealed multiple approaches using teams of health care providers with 72.7% of the studies reporting statistically significant results in knowledge, practice and/or outcomes. Of 10 dissemination strategies the most effective were reminders, and audit and feedback. The most popular strategy was education meetings. A secondary analysis revealed different populations with chronic or complex disorders where a team approach was effective in practice guideline dissemination and implementation. Conclusions Many of the studies provided caveats to explain how or why the strategies did or did not demonstrate improvements. Overall, authors described complex health care requiring increasingly complex approaches to ensure evidence based guidelines were utilised in practice, including using multiple dissemination and implementation strategies. The review has provided evidence that a multi-pronged approach to dissemination and implementation of practice guidelines will assist in gaining significant improvements in change in knowledge, practice and patient outcomes.
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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 | 0.048 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 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".