P212 Review of Systematic Reviews Related to Clinical Guidelines Implementation
Bibliographic record
Abstract
Background Clinical guidelines should be implemented using evidence-based implementation strategies. However, guideline development programmes rarely allocate resources to perform evidence-based reviews of implementation strategies required to implement their guidelines. A streamlined approach to obtain such summaries of evidence in preparation for development of cardiovascular risk reduction guidelines sponsored by a national-level organisation was a review of systematic reviews (SRs) of implementation strategies. Objectives To explore whether SRs of implementation strategies provide support for the effectiveness of these strategies. Methods Rx for Change database of the Canadian Agency for Drugs and Technologies in Health (CADTH) was selected a priori as data source for this review of systematic reviews. The review was limited to high quality SRs of interventions targeting clinicians. Results A total of 12 SRs met study inclusion criteria. These SRs suggest that implementation strategies, such as audit and feedback, academic detailing, and educational meetings, are generally effective in improving providers’ behaviours, with small to moderate effect sizes. Discussion This review of SRs provides support for the overall efficacy of guideline implementation strategies, while highlighting the need for further comparative and cost effectiveness research to address gaps in the knowledge identified (e.g., limited information on head-to-head comparisons between strategies, clinical context, and cost of interventions). Implications for Guideline Developers/Users Guideline developers should include recommendations for guideline implementation in their future guidelines. Making specific recommendations on choosing one implementation strategy over the others should be avoided until further head-to-head comparisons are available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.351 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".