LO14: The CanadiEM Digital Scholars Program: An innovative international digital collaboration curriculum
Bibliographic record
Abstract
Introduction/Innovation Concept: Digital media are a new frontier in medical education scholarship. Asynchronous education resources facilitate a multi-modal approach to teaching, and allows residents to personalize their learning to achieve mastery in their own time. The CanadiEM Digital Scholars Program is a nationwide initiative that provides residents with practical experiences in creating digital educational materials under the supervision of experts in the field. The program allows for collaboration and access to mentorship from top digital educators from across North America. Methods: Interested residents accepted into the program spent a period of their PGY4 year completing modules developed in the theory and science behind digital education. Four modules, developed in an iterative process, have been built on the topics of podcasting, blogging, digital identity, and patient communication. Each fellow was supervised members of the CanadiEM team, a faculty member from the resident’s home institution, and digital experts from across North America. Curriculum, Tool, or Material: The first fellow completed all aspects of the designed curriculum. Above this, he also engaged in blog content creation, initiated research on digital scholarship, and managed the editorial section of CanadiEM. The second fellow is currently halfway through his year (and is expected to complete the program within the year) and has co-authored 30 blog posts and 53 podcasts in 6 months. Conclusion: The CanadiEM Digital Scholars Program utilizes a novel approach to foster development of digital educators utilizing experts across North America. We have demonstrated the feasibility and sustainability with our initial pilot years. This program is being scaled next year to include two scholars per year, which will facilitate cross-collaboration between the scholars.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".