Narrative Medicine, Healing and Salutogenesis: Contrasting meta-narratives
Bibliographic record
Abstract
While narrative medicine “fortifies clinical practice with the narrative competence to recognize, absorb, metabolize, interpret, and be moved by stories of illness”, (www.narrativemedicine.org), the role of healing in clinical narratives has yet to be considered. Salutogenesis, defined by the late Anton Antonovsky, is the ‘creation of health and the fostering of healing’ where healing does not simply address reversal of disease but emphasizes health-promoting experiences and behaviors as distinct and differentiated from the illness perspective. This contrast between illness and healing offers an opportunity to consider a deeper meta-narrative (identity stories that confer legitimacy on what we do) – that can significantly broaden and likely add to the impact of narrative medicine. In this 45-minute workshop, Drs. Kreisberg and Huffaker will guide participants through two narrative exercises – the first, an illness narrative, and second, a healing narrative. By introducing Antonovsky’s perspective on healing, participants will gain an awareness of a deep and widespread meta-narrative comparison that has yet to be articulated in narrative medicine. In contrasting these meta-narratives, it is suggested that work has yet to be done to fully explore the potential for healing narratives in narrative medicine. They can afford the exploration of stories far beyond the bio-medical illness paradigm and introduce the likelihood that including both illness and healing stories offers an enriched potential for narrative elements of whole person care.
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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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".