The language of care: narrative medicine
Bibliographic record
Abstract
Narrative medicine is based on authentic stories of illness, health and well-being and not on literary artefacts, which instead belong to the world of humanities for health. Why study narratives in the world of healthcare? The Authors answer this question by stating that the narrative medicine gives us back how the sick person lives, in compliance with the holistic model of care (biological, psychological, social and spiritual) proposed by the WHO. The Authors highlight three Areas of narrative medicine application: a. clinical practice, where it facilitates understanding between doctor and patient and guide towards a holistic approach to care, aimed at the search for well-being; b. training: the institution for some years in some American, Canadian and European universities (including Italian ones), of training courses in narrative medicine and medical humanities overcomes the technicality of the biomedical model and build a professionalism that possesses not only technical skills, but also relational skills. c. research, where creates a series of opportunities, that can generate new hypotheses and helping to define patient-centered guidelines. In conclusion, narrative medicine is an operation of listening and observation that improves the relationship through empathy and is able to contribute, through research, to the evolution of clinical, care, therapeutic, psychological, social, spiritual aspects, promoting the well-being of patients, their loved ones and their caregivers.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".