A faculty development workshop in narrative-based reflective writing
Bibliographic record
Abstract
Narrative approaches are used increasingly in the health professions with a range of objectives. We must acquaint educators with this burgeoning field and prepare them for the incorporation of story-telling in their pedagogical practices. The authors describe a template for a faculty development workshop designed to foster self-reflection through the use of narrative techniques and prepare clinical teachers to deploy such approaches. The design is based on a six-year experience in delivering introductory workshops in narrative approaches to medical teachers. The workshops, which served as a model for the template, have been offered to a total of 92 clinicians being trained to mentor medical students. A generic template is described. It includes a table of core concepts from narrative theory, a set of probing questions useful in a basic technical analysis of texts and a list of initiating prompts for exercises in reflective writing. A workshop organized and deployed using this template is deliverable over a half-day. The model has proven to be feasible and highly valued by participants. It can be adapted for other contexts by educators across the continuum of health professional 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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".