LO54: The CanadiEM Junior Editor program: a quantitative study and program evaluation
Bibliographic record
Abstract
Introduction: CanadiEM.org is a multi-author open access medical education website which aims to improve emergency care in Canada by building an online community of practice for healthcare practitioners and providing them with high quality, freely available educational resources. It is used by physicians, allied health professionals, and trainees globally. Junior (medical student and/or resident) Editors are key members of the community who are mentored to advance their academic skills and knowledge for their careers and the healthcare field. The program also aims to increase the sustainability of the CanadiEM project by supporting the creation and publishing of online content. We aimed to assess the impact and efficacy of this program while discovering ways to improve it. Methods: The experience of all current and previous Junior Editors were assessed through a survey developed by the authorship team for this purpose. The survey consisted of 48 questions, including 15 multiple choice questions rated using a Likert Scale, 10 open-ended questions, and 23 demographic or binary yes/no questions. The participants' perceptions of their experience, desire for future involvement, and opinions regarding implementation of the program at other medical education websites were assessed using open-ended qualitative questions. These responses were thematically analyzed. Results: A total of 28 Junior Editors responded (71.7% of those surveyed). They listed their responsibilities as uploading/copyediting posts, authorship of posts, infographic creation, social media promotion, authorship of podcast summaries, editing of podcasts, and logo design. Results revealed a positive experience across all domains, with participants citing a better experience when compared to previous similar roles. 85.7% (24/28) stated they achieved their expectations from the program, and 82.1% (23/28) would incorporate this program into another medical education website if given the opportunity. Conclusion: Junior Editors reported positive experiences across all responsibilities, with particular value placed on digital and authorship skills development, inspiration for future FOAMed, research engagement, and mentorship/networking. Through collaboration with current team members, we will implement improvement initiatives. Based upon these results, we believe that the Junior Editor model may also be viable within other medical education communities.
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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 | 0.045 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".