Pedagogic Strategies and Contents in Medical Writing/Publishing Education: A Comprehensive Systematic Survey
Bibliographic record
Abstract
Workshops or training sessions on medical writing and publishing exist worldwide. We aimed to evaluate published articles about such workshops and examine both the content and teaching strategies employed. We searched ISI Web of Science, Ovid EMBASE, ERIC, Ovid Medline, and the grey literature. We considered no language, geographical location, or time period limitations. We included randomized controlled trials, before-after studies, surveys, cohort studies, and program evaluation and development studies. We descriptively reported the results. Out of 222 articles that underwent a full-text review, 30 were deemed eligible. The educational sessions were sporadic, with researchers often developing their own content and methods. Fifteen articles reported teaching the standard structure of medical articles, ten articles reported on teaching optimal English language use for writing articles, nine articles discussed publication ethics issues, and three articles discussed publication strategies to enhance the chance of publication. Most reports lacked in-depth descriptions of the content and strategies used, and the approach to those topics was relatively superficial. Existing workshops have covered topics such as the standard structure of articles, publication ethics, techniques for improving publication rates, and how to use the English language. However, many other topics are left uncovered. The reports and practice of academic-teaching courses should be improved.
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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 | 0.027 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".