The use of metaphors by service users with diverse long-term conditions: a secondary qualitative data analysis
Bibliographic record
Abstract
Long-term conditions and accompanied co-morbidities now affect about a quarter of the UK population. Enabling patients and caregivers to communicate their experience of illness in their own words is vital to developing a shared understanding of the condition and its impact on patients' and caregivers' lives and in delivering person-centred care. Studies of patient language show how metaphors provide insight into the physical and emotional world of the patient, but such studies are often limited by their focus on a single illness. The authors of this study undertook a secondary qualitative data analysis of 25 interviews, comparing the metaphors used by patients and parents of patients with five longterm conditions. Analysis shows how similar metaphors can be used in empowering and disempowering ways as patients strive to accept illness in their daily lives and how metaphor use depends on the manifestation, diagnosis, and treatment of individual conditions. The study concludes with implications for how metaphorical expressions can be attended to by healthcare professionals as part of shared care planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".