Trends in guideline implementation: a scoping systematic review
Bibliographic record
Abstract
BACKGROUND: There is currently no reliable way to choose strategies that are appropriate for implementing guidelines facing different barriers. This study examined trends in guideline implementation by topic over a 10-year period to explore whether and how strategies may be suitable for addressing differing barriers. METHODS: A scoping systematic review was performed. MEDLINE and EMBASE were searched from 2004 to 2013 for studies that evaluated the implementation of guidelines on arthritis, diabetes, colorectal cancer and heart failure. Data on study characteristics, reason for implementation (new guideline or quality improvement), implementation strategy used, rationale for selecting that strategy and reported impact were extracted and summarized. Interventions were mapped against a published taxonomy of guideline implementation strategies. RESULTS: The search resulted in 1,709 articles; 156 were retrieved and 127 were excluded largely because they did not evaluate guideline implementation, leaving 32 eligible for review (4 arthritis, 3 colorectal cancer, 21 diabetes, 4 heart failure). Six of 7 randomized trials and 8 of 25 observational studies had a low risk of bias. Most studies promoted guideline use for quality improvement (78.0%). Few studies rationalized strategy choice (18.8%). Most employed multiple approaches and strategies, most often educational meetings and print material for professionals or patients. Few studies employed organizational, financial or regulatory approaches. Strategies employed that were unique to the published taxonomy included professional (print material, tailoring guidelines, self-audit training or material) and patient strategies (education, counselling, group interaction, print material, reminders). Most studies achieved positive impact (87.5%). This did not appear to be associated with guideline topic, use of theory or barrier assessment, or number or type of implementation approaches and strategies. CONCLUSIONS: While few studies were eligible, limiting insight on how to choose implementation strategies that address guideline-specific barriers, this review identified other important findings. Education for professionals or patients and print material were the most commonly employed strategies for translating guidelines to practice. Mapping of strategies onto the published taxonomy identified gaps in guideline implementation that represent opportunities for future research and expanded the taxonomy.
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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 | 0.068 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.043 | 0.053 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".