Electronic continuing education in the health professions: An update on evidence from RCTs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it