Enhancing the care of children with chronic diseases through the narratives of patient, physician, nurse and carer
Bibliographic record
Abstract
We tested the hypothesis that a narrative approach may enhance a bio-psycho-social model (BPS) in caring for chronically ill children. Forty-eight narratives were collected from 12 children with six different medical conditions, their mothers, physicians, and nurses. By a textual analysis, narratives were classified on their predominant focus as disease (biological focus), illness (psychologic focus), or sickness (social focus). Sixty-one percent of narrative' text were classified as illness, 28% as disease and 11% as sickness. All narratives had a degree of illness focus. Narratives by patients and physicians on the one hand, and nurses' and mothers' on the other were disease focused. Narratives were also evaluated with respect to the type of medical condition: Illness was largely prevalent in all but Crohn's disease and HIV infection, the latter having a predominance of sickness most probably related to stigma. Narrative exploration proved a valuable tool for understanding and addressing the needs of children with complex conditions. Narrative approaches allow identification of the major needs of different patients according to health conditions and story tellers. In the narratives, we found a greater illness and disease focus and surprisingly a low sickness focus, except with HIV stories. Narrative medicine provides a tool to strengthen the BPS model in health 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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".