The writer’s guide to education scholarship in emergency medicine: Education innovations (part 3)
Bibliographic record
Abstract
OBJECTIVE: The scholarly dissemination of innovative medical education practices helps broaden the reach of this type of work, allowing scholarship to have an impact beyond a single institution. There is little guidance in the literature for those seeking to publish program evaluation studies and innovation papers. This study aims to derive a set of evidence-based features of high-quality reports on innovations in emergency medicine (EM) education. METHODS: We conducted a scoping review and thematic analysis to determine quality markers for medical education innovation reports, with a focus on EM. A search of MEDLINE, EMBASE, ERIC, and Google Scholar was augmented by a hand search of relevant publication guidelines, guidelines for authors, and website submission portals from medical education and EM journals. Study investigators reviewed the selected articles, and a thematic analysis was conducted. RESULTS: Our search strategy identified 14 relevant articles from which 34 quality markers were extracted. These markers were grouped into seven important themes: goals and need for innovation, preparation, innovation development, innovation implementation, evaluation of innovation, evidence of reflective practice, and reporting and dissemination. In addition, multiple outlets for the publication of EM education innovations were identified and compiled. CONCLUSION: The publication and dissemination of innovations are critical for the EM education community and the training of health professionals. We anticipate that our list of innovation report quality markers will be used by EM education innovators to support the dissemination of novel educational practices.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.063 | 0.046 |
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".