Effective learning environments – the process of creating and maintaining an online continuing education tool
Bibliographic record
Abstract
Continuing medical education (CME) is an indispensable part of maintaining physicians' competency. Since attending conferences requires clinical absenteeism and is not universally available, online learning has become popular. The purpose of this study is to conduct a retrospective analysis examining the creation process of an anesthesia website for adherence to the published guidelines and, in turn, provide an illustration of developing accredited online CME. Using Kern's guide to curriculum development, our website analysis confirmed each of the six steps was met. As well, the technical design features are consistent with the published literature on efficient online educational courses. Analysis of the database from 3937 modules and 1628 site evaluations reveals the site is being used extensively and is effective as demonstrated by the participants' examination results, content evaluations and reports of improvements in patient management. Utilizing technology to enable distant learning has become a priority for many educators. When creating accredited online CME programs, course developers should understand the educational principles and technical design characteristics that foster effective online programs. This study provides an illustration of incorporating these features. It also demonstrates significant participation in online CME by anesthesiologists and highlights the need for more accredited programs.
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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 | 0.017 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".