Effective hands-on teaching strategy on participants’ confidence in medical writing and publishing: A before-after study
Bibliographic record
Abstract
Proficiency in medical writing is crucial for disseminating reports of medical studies. The impact of workshops in this regard on participants' confidence is a subject of debate. We assessed the impact of a hands-on workshop on participants' confidence in medical writing. Participants of a 2-day "learning-by-doing" workshop held at McMaster University participated in this before-after study. We used a unique, reliable, and valid tool comprising two domains of confidence in medical writing and using English language before and after receiving the educational intervention. Of 25 participants, 21 completed the instrument before and after the workshop. Typical participants were female, and students in their 30s, who had not attended a prior workshop. The mean (95% CI) increase in the participants' confidence for domain 1 was 15.3 (10.5, 20.1), for domain 2 was 16.8 (9.8, 23.8), and for the total score was 32.1 (20.9, 43.2) (all P<0.001). Between-subgroup analyses showed the score increase was significantly higher in participants with less than 5 years of experience in medical research. The workshop had a positive impact on enhancing participants' confidence in writing skills, including using active verbs, crafting short sentences, summarizing main findings, and adhering to checklists like CONSORT. Hands-on medical writing workshops can boost participants' confidence in writing medical articles and using optimal English language. Targeting junior researchers and graduate students could result in a better outcome. Emphasizing the writing areas where participants achieved higher score changes might yield better outcomes for such workshops.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".