Teaching an intensive core course for medical students in the era of Covid-19: Mindful Medical Practice on Zoom
Bibliographic record
Abstract
Background The COVID19 pandemic brought many challenges, including delivering interactive courses such as the Mindful Medical Practice (MMP) program to medical students. It also provided opportunities to trial online teaching of the program using technologies such as Zoom. Approach Medical educators from McGill University in Montreal and The Rural Clinical School of Western Australia in Busselton collaborated via Zoom to adapt the MMP program to an online format. This involved weekly meetings to adapt each class and debrief following its delivery. A number of adaptations were required which were implemented with ease while maintaining the program’s integrity. Evaluation The facilitator found the course relatively straightforward to teach with Zoom. In their essays at the end of the coursethe students reported that the MMP program was a valuable experience that they found to be “enjoyable”, “positive”, “interesting”, “beneficial” and “refreshing”. They reported that the online experience offered benefits over face-to-face delivery and was particularly helpful during the COVID19 pandemic. Reflection There were a number of potential limitations: this was a relatively small group of students; the students were already well acquainted with the facilitator; the students and the facilitator were experienced in using Zoom for teaching. The major strength was a clear demonstration of the feasibility of delivering the entire program online that is particularly relevant during this time of stress and uncertainty and also expands the potential to provide this teaching to students and universities across the world.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".