The transition from face-to-face to online CME facilitation
Bibliographic record
Abstract
This study examines the experiences of nine medical teachers who transitioned from face-to-face teaching to facilitating a course in an online environment. The authors examined the reasons why the teachers agreed to facilitate an online course, the challenges they encountered and their practical solutions, and the advantages and disadvantages they perceived to this teaching environment. Thirty-minute phone interviews were conducted. An iterative process was used to develop the themes and sub-themes for coding. Teachers reported being attracted to the novelty of the new instructional format and saw online learning as an opportunity to reach different learners. They described two facets to the transition associated with the technical and facilitation aspects of online facilitation. They had to adapt their usual teaching materials and determine how they could make the 'classroom' user friendly. They had to determine ways to encourage interaction and facilitate learning. Lack of participation was frustrating for most. This study has implications for those intending to develop online courses. Teacher selection is important as teachers must invest time in course development and teaching and encourage participation. Teacher support is critical for course design, site navigation and mentoring to ensure teachers facilitate online discussion.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".