Canadi <scp>EM</scp> : 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 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".