Comparative effectiveness of educational interventions in neurological disease for healthcare workers and students: a systematic review.
Bibliographic record
Abstract
OBJECTIVES: To assess the comparative effectiveness of educational interventions in neurological disease for healthcare workers and students. DESIGN: Systematic review. DATA SOURCES: Medline, Embase and Cochrane through to 1 June 2025. ELIGIBILITY CRITERIA: Studies evaluating neurological disease educational interventions with a comparator group (observational cohort/randomised controlled trial (RCT)) were included. DATA EXTRACTION AND SYNTHESIS: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-compliant systematic review was conducted (PROSPERO: CRD42023461838). Knowledge acquisition and educational methodologies were collected from each study. Study outcomes were classified using the Kirkpatrick and Kirkpatrick four-level model (learner reaction, knowledge acquisition, behavioural change, clinical outcome).1 Risk of bias was assessed using the Newcastle-Ottawa scale for non-randomised studies and the Cochrane Risk of Bias tool for RCTs.2 3 RESULTS: A total of 67 studies involving 4728 participants were included. Of these, 36 were RCTs, and 31 were observational studies. Virtual interventions were the most common (67.2%, n=45 studies), primarily targeting either medical students (46.3%, n=31 studies) or specialists (40.3%, n=27 studies). Overall, 70.1% (n=47) of studies demonstrated outcomes in favour of the intervention. However, few studies used K&K level 3/4 outcomes, with two studies evaluating behaviour change (level 3) and three assessing clinical outcomes (level 4 combined with other levels). No study exclusively assessed level 4 outcomes. Meta-analysis of 22 RCTs with calculable standardised mean differences (SMDs) (n=1748) showed a significant benefit of interventions (SMD 0.75, 95% CI 0.22 to 1.27, p=0.0056). CONCLUSIONS: This review highlights a growing body of research particularly focusing on virtual techniques, specialist audiences and treatment-oriented content. Few studies assessed changes in practice or patient care. Non-specialists remain underrepresented. Future studies should prioritise assessing the clinical impact of educational interventions within non-specialist audiences.
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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 | 0.027 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".