Evidence-Based Strategies for Implementing Guidelines in Obstetrics
Bibliographic record
Abstract
OBJECTIVE: To estimate effective strategies for implementing clinical practice guidelines in obstetric care and to identify specific barriers to behavior change and facilitators in obstetrics. DATA SOURCES: The Cochrane Library, EMBASE, and MEDLINE were consulted from January 1990 to June 2005. Additional studies were identified by screening reference lists from identified studies and experts' suggestions. METHODS OF STUDY SELECTION: Studies of clinical practice guidelines implementation strategies in obstetric care and reviews of such studies were selected. Randomized controlled trials, controlled before-after studies, and interrupted time series studies were evaluated according to Effective Practice and Organization of Care criteria standards. TABULATION, INTEGRATION, AND RESULTS: Studies were reviewed by two investigators to assess the quality and the efficacy of each strategy. Discordances between the two reviewers were resolved by consensus. In obstetrics, educational strategies with medical providers are generally ineffective; educational strategies with paramedical providers, opinion leaders, qualitative improvement, and academic detailing have mixed effects; audit and feedback, reminders, and multifaceted strategies are generally effective. These findings differ from data on the efficacy of clinical practice guidelines implementation strategies in other medical specialties. Specific barriers to behavior change in obstetrics and methods to overcome these barriers could explain these differences. The proportion of effective strategies is significantly higher among the interventions that include a prospective identification of barriers to change compared with standardized interventions. CONCLUSION: Prospective identification of efficient strategies and barriers to change is necessary to achieve a better adaptation of intervention and to improve clinical practice guidelines implementation. In the field of obstetric care, multifaceted strategy based on audit and feedback and facilitated by local opinion leaders is recommended to effectively change behaviors.
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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 | 0.080 | 0.302 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".