Narrative medicine and narrative practice: partners in the creation of meaning
Bibliographic record
Abstract
Background Narrative medicine has emerged as an approach to whole person care and to support the clinician-patient therapeutic relationship. Although training in narrative medicine is usually based on the study of literary or artistic works, the same attitude of close reading can also be applied in conversations with patients or learners.MethodWe held a two-day narrative medicine workshop, incorporating two approaches: 'Conversations Inviting Change' (CIC) and humanities-based narrative medicine as taught by Columbia University. The workshop was primarily experiential, with theoretical components of both approaches. Participants brought active concerns for confidential breakout sessions and engaged in text-based and reflective writing exercises. Participants generated metaphors to describe these approaches to narrative medicine.Results Participants included a mix of community and hospital-based practitioners, pre-dominantly doctors. Participants considered the two approaches to be compatible and enhance each other. One metaphor generated was that Columbia style narrative medicine is ’like an individual lens which allows you to see things clearer’, it allows practitioners a different perspective on their patients and that CIC teaching ‘is a frame of glasses in which the lenses could be placed to enhance the ease of use’. Another metaphor was that the former ‘is like learning from a cadaver in the anatomy lab’, while the latter ‘is like running a clinical simulation’.Conclusion We believe this was the first workshop integrating these approaches to narrative medicine. They appear to be highly complementary. Both approaches lead to enhanced attention to narratives which has clear applicability to clinical practice.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".