Converting to Connect: A Rapid RE‐AIM Evaluation of the Digital Conversion of a Clerkship Curriculum in the Age of COVID‐19
Bibliographic record
Abstract
Abstract Background With the advent of the 2019 coronavirus pandemic, a decision was made to remove medical students from clinical rotations for their own safety. This forced students on a core emergency medicine (EM) rotation at McMaster University to immediately cease all in‐person activities. An urgent need for a virtual curriculum emerged. Methods A virtual curriculum consisting of asynchronous case‐based learning on Slack, ask‐me‐anything webinars, and online e‐modules was created to fill the need. We describe a program evaluation using the RE‐AIM framework and a social networking analysis of participants. Results Medical students (n = 23) and 11 facilitators (five residents, six faculty members) participated in this pilot study. Faculty members sent a mean (±SD) of 115 (±117) messages (n = 6), and mean (±SD) message counts for students and residents were 49.96 (±25; n = 23) and 39 (±38; n = 5), respectively. A total of 62,237 words were written by the participants, with a mean of 1,831 per person. Each message consisted of a mean (±SD) of 25 words (±29). Students rapidly acquitted themselves to digital technology. Using the RE‐AIM framework we highlight the feasibility of a virtual curriculum, discuss demands on faculty time, and reflect on strategies to engage learners. Conclusions The use of asynchronous digital curricula creates opportunities for faculty–resident interaction and engagement. We report the successful deployment of a viable model for undergraduate EM training for senior medical students in the COVID‐19 era of physical distancing.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".