CanadiEM: Accessing a Virtual Community of Practice to Create a Canadian National Medical Education Institution
Bibliographic record
Abstract
Background The rise of free open‐access medical education (FOAM) has led to a wide range of online resources in emergency medicine. Canadian physicians have been active contributors to FOAM. Objectives We aimed to create a virtual community of practice that would serve as a national platform for collaboration, learning, and knowledge dissemination. Methods CanadiEM was formed in 2016 from the merger of two Canadian websites and a podcast. Using a community‐of‐practice model, we introduced two training programs to support junior community members in becoming core editorial team members and employed asynchronous Web technologies to facilitate collaboration. We also introduced a coached peer review process and formed strategic alliances that aim to ensure a high quality of publication. Results CanadiEM has become a portal for readers to access a broad range of FOAM content. The website has published 782 articles. Of these, 71 have undergone a coached peer review process. The website has received over 2.5 million page views from 217 countries, and the associated CRACKCast podcast has been downloaded over 750,000 times. Conclusions CanadiEM has succeeded in building a national multi‐interface dissemination network that fosters collaboration and knowledge sharing in emergency medicine while fostering junior digital scholars. The construction of a community of practice has been facilitated by quality assurance, training programs, and the use of asynchronous Web technologies. Ongoing challenges in sustainability include a volunteer workforce with high turnover.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.007 |
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".