eLearning: A review of Internet-based continuing medical education
Bibliographic record
Abstract
INTRODUCTION: The objective was to review the effect of Internet-based continuing medical education (CME) interventions on physician performance and health care outcomes. METHODS: Data sources included searches of MEDLINE (1966 to January 2004), CINAHL (1982 to December 2003), ACP Journal Club (1991 to July/August 2003), and the Cochrane Database of Systematic Reviews (third quarter, 2003). Studies were included in the analyses if they were randomized controlled trials of Internet-based education in which participants were practicing health care professionals or health professionals in training. CME interventions were categorized according to the nature of the intervention, sample size, and other information about educational content and format. RESULTS: Sixteen studies met the eligibility criteria. Six studies generated positive changes in participant knowledge over traditional formats; only three studies showed a positive change in practices. The remainder of the studies showed no difference in knowledge levels between Internet-based interventions and traditional formats for CME. DISCUSSION: The results demonstrate that Internet-based CME programs are just as effective in imparting knowledge as traditional formats of CME. Little is known as to whether these positive changes in knowledge are translated into changes in practice. Subjective reports of change in physician behavior should be confirmed through chart review or other objective measures. Additional studies need to be performed to assess how long these new learned behaviors could be sustained. eLearning will continue to evolve as new innovations and more interactive modes are incorporated into learning.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".