The writer's guide to education scholarship in emergency medicine: Systematic reviews and the scholarship of integration (part 4)
Bibliographic record
Abstract
Objective: Reviews help scholars consolidate evidence and guide their educational practice. However, few papers describe how to effectively publish review papers. We completed a scoping review to develop a set of quality indicators that will assist junior authors to publish reviews and integrative scholarship. Methods: MEDLINE, Embase, ERIC, and Google Scholar were searched for English language articles published between 2012 and January 2016 using the terms review, medical education, how to publish, and emergency medicine. Titles and abstracts were reviewed by two authors and included if they focused on how to publish a review or outlined reporting guidelines of reviews. The articles were reviewed in parallel for calibration, and disagreements were resolved through a consensus. Results: A full text review of the 25 articles was conducted, and 196 recommendations were extracted from 13 articles. A hand search of the included articles' reference lists and expert recommendation found an additional eight articles. These recommendations were thematically analysed into a list of seven themes and 32 items. Additionally, seven evaluation tools and reporting guidelines were found to guide researchers in optimizing their reviews for publication. Conclusion: In emergency medicine education, review articles can help synthesize educational research so that educators can engage in evidence-based scholarly teaching. We hope that this work will act as an introduction to those interested in engaging in integrative scholarship by providing them with a guide to key quality markers and important checklists for improving their research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.111 | 0.369 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.036 | 0.034 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.034 | 0.022 |
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".