A guide to peer reviewing medical education scholarship: Advice from editors of AEM Education and Training
Bibliographic record
Abstract
[para. 1]: "Peer reviews in medical education can seem intimidating and the quality of reviews can vary widely. Many journals (including Academic Emergency Medicine Education and Training [AEM E&T]) provide scores for peer reviewers based on the quality of their reviews. While we share our reviewer scores each year, reviewers have asked how they could improve their reviewer scores and the strength of their reviews. As members of the editorial board for AEM E&T, we sought to share our insights and advice for creating a high-quality peer review of medical education research. To address this need, we identified some of the common reasons for lower scoring reviews and features of higher scoring reviews to guide reviewers in the spirit of education and continual improvement."
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.117 | 0.424 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.020 | 0.079 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".