Interactive on-line continuing medical education: Physicians' perceptions and experiences
Bibliographic record
Abstract
INTRODUCTION: Although research in continuing medical education (CME) demonstrates positive outcomes of on-line CME programs, the effectiveness of and learners' satisfaction with interpersonal interaction in on-line CME are lower Defined as faculty-learner or learner-learner interpersonal interaction, this study explores physicians' perceptions of and experiences in interactive on-line CME and factors influencing these. METHODS: Focus groups and interviews were undertaken by three Canadian universities. Using purposive sampling, we recruited physicians based on their experiences with interactive on-line CME. Content analysis was applied first, followed by a comparative analysis to confirm themes and findings. RESULTS: Physicians based their perceptions of interactive on-line CME by comparing it with what they know best, face-to-face CME. Although perceptions about access and technical competency remained important, two other themes emerged. The first was the capacity of on-line CME to meet individual learning preferences, which, in turn, was influenced by the quality of the program, the degree of self-pacing or self-direction, opportunity for reflection, and educational design. The second was the quality and quantity of interpersonal interaction, which was shaped by perceptions of social comfort, the educational value of interactions, and the role of the facilitator. Prior experience with on-line CME moderated perceptions. DISCUSSION: The extent that on-line CME programs reflected characteristics of high-quality CME and individual learning preferences appeared to shape perceptions about it. It is important to incorporate the characteristics of effective CME into the design and implementation of interactive on-line programs, considering diverse learning preferences, providing faculty development for on-line facilitators, and grounding this work in learning theory.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".