Making sense of illness in the absence of diagnosis : patients' and physicians' narratives of medically unexplained symptoms
Bibliographic record
Abstract
The aim of this study was to improve medical understanding of patients' illness experience and everyday thinking about physical distress, by focusing on patients' and physicians' narratives of medically unexplained symptoms. Semi-structured interviews were held with 16 Canadian and immigrant patients from two primary care clinics in Montreal, and separately with their physician. Detailed content analysis reveals that, despite the absence of diagnosis, both patient and physicians hold complex and dynamic models of illness. Physicians' explanations rely almost exclusively on biomedical constructs, whereas patients' models of illness draw from a much wider range of sources of experience and authority. Despite regular follow-up, physicians have very limited access to the intricate networks of meaning revealed in their patients' interviews. In fact, although there is some common ground of understanding, patients and physicians show low congruence of their models, and much discrepancy in the expected outcome and management. Eliciting patients' illness narratives rather than focusing on narrow biomedical issues offers promising possibilities for physicians to negotiate meaning with their patients. The richness of patients' models provides potential avenues leading out of the clinical impasse of medically unexplained symptoms.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".