Peer-support writing group in a community family medicine teaching unit
Bibliographic record
Abstract
Problem addressed Aspiring physician writers need an environment that promotes self-reflection and can help them improve their skills and confidence in writing. Objective of program To create a peer-support writing group for physicians in the Markham-Stouffville community in Ontario to promote professional development by encouraging self-reflection and fostering the concept of physician as writer. Program description The program, designed based on a literature review and a needs assessment, was conducted in 3 sessions over 6 months. Participants included an emergency physician, 4 family physicians, and 3 residents. Four to 8 participants per session shared their projects with guest physician authors. Eight pieces of written work were brought to the sessions, 3 of which were edited. A mixed quantitative and qualitative evaluation model was used with preprogram and postprogram questionnaires and a focus group. Conclusion This program promoted professional development by increasing participants’ frequency of self-reflection and improving their proficiency in writing. Successful elements of this program include creating a supportive group environment and having a physician-writer expert facilitate the peer-feedback sessions. Similar programs can be useful in postgraduate education or continuing professional development.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".