Short report: satisfaction with on-line CME. Evaluation of the ruralMDcme website.
Bibliographic record
Abstract
n recent years, use of the World Wide Web as a means of providing lifelong learning opportunities has increased. The main benefits of on-line continuing medical education (CME) include easy access, convenience, cost-eff ectiveness, reduced travel, self-paced and self-directed learning, and an interactive multimedia format.1-3 Several on-line CME studies4-6 have reported satisfaction with Internet learning and substantial acquisition of knowledge. An interesting aspect of the on-line CME literature is the diverse nature of the delivery formats that have been described. On-line CME has been delivered by real-time Internet teleconferencing, live and delayed audio and video CME Web broadcasts, and problem-based learning discussion system designs. In spring 2002, Memorial University of Newfoundland in St John’s led a consortium of Canadian university-based CME departments in the development of RuralMDcme, a CME website that provides accredited on-line CME courses by the College of Family Physicians of Canada. The purpose of this study was to evaluate physicians’ satisfaction with an on-line CME format that used the WebCT learning management system and facilitated interaction using computer-mediated discussion. METHODS
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".