A review of evaluation outcomes of web‐based continuing medical education
Bibliographic record
Abstract
INTRODUCTION: The Internet and worldwide web have expanded opportunities for the provision of a flexible, convenient and interactive form of continuing medical education (CME). Larger numbers of doctors are accessing and using the Internet to locate and seek medical information. It has been suggested that a significant proportion of this usage is directly related to questions that arise from patient care. A variety of Internet technologies are being used to provide both asynchronous and synchronous forms of web-based CME. Various models for designing and facilitating web-based CME learning have also been reported. The purpose of this study was to examine the nature and characteristics of the web-based CME evaluative outcomes reported in the peer-reviewed literature. METHODS: A search of Medline was undertaken and the level of evaluative outcomes reported was categorised using Kirkpatrick's model for levels of summative evaluation. RESULTS: The results of this analysis revealed that the majority of evaluative research on web-based CME is based on participant satisfaction data. There was limited research demonstrating performance change in clinical practices and there were no studies reported in the literature that demonstrated that web-based CME was effective in influencing patient or health outcomes. DISCUSSION: The findings suggest an important need to examine in greater detail the nature and characteristics of those web-based learning technologies, environments and systems which are most effective in enhancing practice change and ultimately impacting patient and health outcomes. This is particularly important as the Internet grows in popularity as a medium for knowledge transfer.
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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 | 0.096 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".