Evaluation of Learning Outcomes in Web-Based Continuing Medical Education
Bibliographic record
Abstract
BACKGROUND: There has been significant growth in use of Web-based continuing medical education (CME) by physicians. A number of evaluation and metareview studies have examined the effectiveness of Web-based CME to varying degrees. One of the main limitations of this literature has been the lack of systematic evaluation across different clinical subject matter areas using standardized Web-based CME learning formats. METHOD: One group of pretest-postest designs were used to evaluate knowledge and self-reported confidence change across multiple Web-based courses using a standardized instructional format but comprising distinct clinical subject matter. Participants also completed a participant satisfaction survey and a self-reported retrospective skill/ability change survey. RESULTS: The majority of courses evaluated demonstrated significant pre to post knowledge and confidence effect size change, as well as significant self-reported retrospective practice change. CONCLUSIONS: A Web-based CME instructional format comprising multimedia-enhanced learning tutorials supplemented by asynchronous computer-mediated conferencing for case-based discussions was found to be effective in enhancing knowledge, confidence, and self-reported practice change outcomes across a variety of clinical subject matter areas.
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.
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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".