Creating pedagogical spaces for developing doctor professional identity
Bibliographic record
Abstract
OBJECTIVES: Working with doctors to develop their identities as technically skilled as well as caring, compassionate and ethical practitioners is a challenge in medical education. One way of resolving this derives from a narrative reflective practice approach to working with residents. We examine the use of such an approach. METHODS: This paper draws on a 2006 study carried out with four family medicine residents into the potential of writing, sharing and inquiring into parallel charts in order to help develop doctor identity. Each resident wrote 10 parallel charts over 10 weeks. All residents met bi-weekly as a group with two researchers to narratively inquire into the stories told in their charts. RESULTS: One parallel chart and the ensuing group inquiry about the chart are described. In the narrative reflective practice process, one resident tells of working with a patient and, through writing, sharing and inquiry, integrates her practice and how she learned to be a doctor in one cultural setting into another cultural setting; another resident affirms her relational way of practising medicine, and a third resident begins to see the complexity of attending to patients' experiences. CONCLUSIONS: The process shows the importance of creating pedagogical spaces to allow doctors to tell and retell, through narrative inquiry, their stories of their experiences. This pedagogical approach creates spaces for doctors to individually develop their own stories by which to live as doctors through narrative reflection on their interwoven personal, professional and cultural stories as they are shaped by, and enacted within, their professional contexts.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".