Impact of Performing Medical Writing/Publishing Workshops: A Systematic Survey and Meta-Analysis
Bibliographic record
Abstract
Objectives: Proficiency in medical writing and publishing is essential for medical researchers. Workshops can play a valuable role in addressing these issues. However, there is a lack of systematic summaries of evidence on the evaluation of their impacts. So, in this systematic review, we aimed to evaluate all articles published on the impact of such workshops worldwide. Methods: We searched Ovid EMBASE, Ovid Medline, ISI Web of Science, ERIC database, and grey literature with no language, time period, or geographical location limitations. Randomized controlled trials, cohort studies, before-after studies, surveys, and program evaluation and development studies were included. We performed a meta-analysis on data related to knowledge increase after the workshops and descriptively reported the evaluation of other articles that did not have sufficient data for a meta-analysis. All analyses were performed using Stata software, version 15.0. Results: Of 23 040 reports, 222 articles underwent full-text review, leading to 45 articles reporting the impacts of workshops. Overall, the reports on the impact of such workshops were incomplete or lacked the necessary precision to draw acceptable conclusions. The workshops were sporadic, and researchers used their own method of assessment. Meta-analyses of the impact on the knowledge showed that workshops could nonsignificantly increase the mean or percentage of participants' knowledge. Conclusion: In the absence of systematic academic courses on medical writing/publishing, workshops are conducted worldwide; however, reports on educational activities during such workshops, the methods of presentations, and their curricula are incomplete and vary. Their impact is not evaluated using standardized methods, and no valid and reliable measurement tools have been employed for these assessments.
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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 | 0.063 | 0.139 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.040 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".