Local opinion leaders: effects on professional practice and health care outcomes
Bibliographic record
Abstract
BACKGROUND: Clinical practice is not always evidence-based and, therefore, may not optimise patient outcomes. Opinion leaders disseminating and implementing 'best evidence' is one innovative method that holds promise as a strategy to bridge evidence-practice gaps. OBJECTIVES: To assess the effectiveness of the use of local opinion leaders in improving the behaviour of health care professionals and patient outcomes. SEARCH STRATEGY: We searched MEDLINE, Health Star, SIGLE and the Cochrane Effective Practice and Organisation of Care Group Trials Register. We did not apply date restrictions to our search strategy. Searches were last updated in February 2005. In addition, we searched reference lists of all potential studies that were identified. SELECTION CRITERIA: Studies eligible for inclusion were randomized controlled trials that used objective measures of performance/provider behaviour and/or patient health outcomes. DATA COLLECTION AND ANALYSIS: Two reviewers extracted data from each study and assessed its methodological quality. We calculated the absolute difference in the risk of 'non-compliance' with desired practice, adjusting for baseline levels of non-compliance where these data were available. MAIN RESULTS: Twelve studies met our eligibility criteria. The adjusted absolute risk difference of non-compliance with desired practice varied from -6% (favouring control) to +25% (favouring opinion leader intervention). Overall, the median adjusted risk difference (ARD) was 0.10 representing a 10% absolute decrease in non-compliance in the intervention group. AUTHORS' CONCLUSIONS: The use of local opinion leaders can successfully promote evidence-based practice. However the feasibility of its widespread use remains uncertain.
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 | 0.021 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".