What can be learned from patient stories about living with the chronicity of heart illness? A narrative inquiry
Bibliographic record
Abstract
BACKGROUND: Patients' illness stories are valuable information that supports person-centred care across the illness trajectory. AIMS: To learn how older South Asian immigrant women experience living with heart illness long after discharge from hospital. METHOD: We used narrative inquiry, a personal experience method that explores and interprets lived and told stories through the three dimensions of experience. DESIGN: Four participants, over the age of sixty, living with heart illness for over ten years, were invited to engage in narrative interview and Narrative Reflective Process. OUTCOMES: Giving patients voice, allows caregivers insight into the human experience of illness beyond hospitalization. Considering the increased migration of people around the globe, this knowledge is significant in provision of person-centred care. IMPLICATIONS: Person-centred care does not end with the hospitalization and outpatient clinics. Inter-disciplinary teams need to reconsider the trajectory of chronic illnesses and the care required throughout, especially for marginalized populations.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".