Videoconferencing for practice-based small-group continuing medical education: Feasibility, acceptability, effectiveness, and cost
Bibliographic record
Abstract
INTRODUCTION: Small-group, practice-based learning is an effective and well-accepted method of continuing medical education (CME). However, one limitation is that many physicians work in communities with fewer than the minimum number recommended for an effective learning group. Videoconferencing has the potential to remove this limitation. The purpose of this study was to evaluate the feasibility, acceptability, effectiveness, and cost of conducting practice-based, small-group CME learning by videoconference. METHODS: Through a videoconferencing link, 10 learners in three communities were guided through four practice-based learning modules by a trained facilitator at a fourth site. Data were collected through evaluation questionnaires, direct observation by the research team, pre- and post-knowledge tests, a focus group, and an interview. RESULTS: A total of 31 learners participated in the four modules. Videoconferencing was generally well accepted by learners. The facilitator and research team observers noted that muting microphones, video quality, audio quality, and audio lag all somewhat hindered discussion. Overall, the facilitator found moderating by videoconference only slightly more difficult than a face-to-face session. There was evidence of knowledge gain, with post-test scores being 20% higher than pretest scores (p = .006). Learners reported nine practice changes from taking the modules. At commercial rates, telecommunications costs per videoconferenced module were approximately CAN$1,200. DISCUSSION: Videoconferencing has the potential to bring the benefits of small-group, practice-based learning to many physicians; however, strict attention to videoconferencing techniques is required. Cost is also an important consideration.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".