Writing for publication in medical education: The benefits of a faculty development workshop and peer writing group
Bibliographic record
Abstract
BACKGROUND: Although educational innovations in medical education are increasing in number, many educators do not submit their ideas for publication. AIMS: The goal of this initiative was to assist faculty members write about their educational innovations. METHOD: Twenty-four faculty members participated in this intervention, which consisted of a half-day workshop, three peer writing groups, and independent study. We assessed the impact of this intervention through post-workshop evaluations, a one-year follow-up questionnaire, tracking of manuscript submissions, and an analysis of curriculum vitae. RESULTS: The workshop evaluations and one-year follow-up demonstrated that participants valued the workshop small groups, self-instructional workbook, and peer support and feedback provided by the peer writing groups. One year later, nine participants submitted a total of 14 manuscripts, 11 of which were accepted for publication. In addition, 10 participants presented a total of 38 abstracts at educational meetings. Five years later, we reviewed the curriculum vitae of all participants who had published or presented their educational innovation. Although the total number of publications remained the same, the number of educationally-related publications and presentations at scientific meetings increased considerably. CONCLUSIONS: A faculty development workshop and peer writing group can facilitate writing productivity and presentations of scholarly work in medical education.
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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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".