Electronic continuing education in the health professions: An update on evidence from RCTs
Bibliographic record
Abstract
INTRODUCTION: Demonstrating the effectiveness of the rapidly expanding field of electronic continuing education (e-CE) has important implications for CE in the health professions. This study provides an update on evidence from randomized controlled trials (RCTs) assessing the effectiveness of e-CE in the health professions. METHODS: A literature search of RCTs was performed in MEDLINE, EMBASE, and CINAHL from 2004 to 2007. Papers were reviewed separately by 2 of the authors and results were categorized and reviewed according to study comparisons. RESULTS: Fifteen studies met our inclusion criteria. Six compared e-CE to no intervention or placebo. Of these 6 studies, 4 showed a statistically significant advantage of the e-CE intervention and 2 showed no significant effect. Two studies compared e-CE to a lecture. Of these, 1 showed an advantage of e-CE and 1 showed no difference. Two studies compared e-CE to a small-group interactive intervention. In both studies, the e-CE group outperformed the control. Two studies compared a multicomponent e-CE intervention to one based on flat text, and both showed the multicomponent intervention to be more effective. Two of the 15 studies demonstrated a statistically significant effect on practice patterns. Positive effects of e-CE on knowledge were shown to persist for up to 12 months and effects on practice up to 5 months. DISCUSSION: Overall, these studies suggest that multicomponent e-CE interventions can be effective in changing health professionals' practice patterns, and improve their knowledge. E-CE interventions based purely on flat text appear to be of limited effectiveness in changing either knowledge or practice. These results support the use of multicomponent e-CE as a method of CE delivery.
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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 | 0.103 | 0.345 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".