CAEP 2016 Academic Symposium: A Writer’s Guide to Key Steps in Producing Quality Education Scholarship
Bibliographic record
Abstract
A key skill for successful clinician educators is the effective dissemination of scholarly innovations and research. Although there are many ways to disseminate scholarship, the most accepted and rewarded form of educational scholarship is publication in peer-reviewed journals. This paper provides direction for emergency medicine (EM) educators interested in publishing their scholarship via traditional peer-reviewed avenues. It builds upon four literature reviews that aggregated recommendations for writing and publishing high-quality quantitative and qualitative research, innovations, and reviews. Based on the findings from these literature reviews, the recommendations were prioritized for importance and relevance to novice clinician educators by a broad community of medical educators. The top items from the expert vetting process were presented to the 2016 Canadian Association of Emergency Physicians (CAEP) Academic Symposium Consensus Conference on Education Scholarship. This community of EM educators identified the highest yield recommendations for junior medical education scholars. This manuscript elaborates upon the top recommendations identified through this consensus-building process.
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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 | 0.029 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.052 | 0.040 |
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".