Local opinion leaders: effects on professional practice and healthcare outcomes
Bibliographic record
Abstract
Cochrane Database of Systematic Reviews Main resultsWe included 24 studies, involving more than 337 hospitals, 350 primary care practices, 3005 healthcare professionals, and 29,167 patients (not all studies reported this information).A majority of studies were from North America, and all were conducted in high-income countries.Eighteen of these studies (21 comparisons, 71 compliance outcomes) contributed to the median adjusted risk difference (RD) for the main comparison.The median duration of follow-up was 12 months (range 2 to 30 months).The results suggested that the OL interventions probably improve healthcare professionals' compliance with evidence-based practice (10.8% absolute improvement in compliance, interquartile range (IQR): 3.5% to 14.6%; moderate-certainty evidence).Results for the secondary comparisons also suggested that OLs probably improve compliance with evidence-based practice (moderatecertainty evidence): i) OLs alone versus no intervention: RD (IQR): 9.15% (-0.3% to 15%); ii) OLs alone versus a single intervention: RD (range): 13.8% (12% to 15.5%); iii) OLs, with a single or more intervention(s) versus the same single or more intervention(s): RD (IQR): 7.1% (-1.4% to 19%); iv) OLs with a single or more intervention(s) versus no intervention: RD (IQR):10.25%(0.6% to 15.75%).It is uncertain if OLs alone, or in combination with other intervention(s), may lead to improved patient outcomes (3 studies; 5 dichotomous outcomes) since the certainty of evidence was very low.For two of the secondary comparisons, the IQR included the possibility of a small negative effect of the OL intervention.Possible explanations for the occasional negative effects are, for example, the possibility that the OLs may have prioritised some outcomes, at the expense of others, or that an unaccounted outcome difference at baseline, may have given a faulty impression of a negative effect of the intervention at follow-up.No study reported on costs or cost-effectiveness.We were unable to determine the comparative effectiveness of different approaches to identifying OLs, as most studies used the sociometric method.Nor could we determine which methods used by OLs to educate their peers were most effective, as the methods were poorly described in most studies.In addition, we could not determine whether OL teams were more effective than single OLs. Authors' conclusionsLocal opinion leaders alone, or in combination with other interventions, can be effective in promoting evidence-based practice, but the effectiveness varies both within and between studies.The effect on patient outcomes is uncertain.The costs and the cost-effectiveness of the intervention(s) is unknown.These results are based on heterogeneous studies differing in types of intervention, setting, and outcomes.In most studies, the role and actions of the OL were not clearly described, and we cannot, therefore, comment on strategies to enhance their effectiveness.It is also not clear whether the methods used to identify OLs are important for their effectiveness, or whether the effect differs if education is delivered by single OLs or by multidisciplinary OL teams.Further research may help us to understand how these factors affect the effectiveness of OLs.
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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 | 0.020 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".