A Narrative Medicine Pilot Study Using the McGill Illness Narrative Interview (MINI) with Patients Suffering from Nephropathy and on Dialysis.
Bibliographic record
Abstract
The present study, that belongs to a wider project intended to the introduction of Narrative Medicine with patients suffering from nephropathy and on dialysis (NeD), aims to explore the experience of patients suffering from NeD and to address physicians in the use of the McGill Illness Narrative Interview (MINI) when they collect patients’ illness narratives in their Narrative Medicine clinical practice. We conducted a narrative research study in October 2015 with the cooperation of an Hospital in Como (Italy). Ten patients suffering from nephropathy and on dialysis were interviewed and their illness narratives were collected using the Italian version of MINI. Then, a thematic analysis was realized referring to the disease, illness and sickness dimensions in relation to main sections of MINI interview. Different perspectives through which these patients feel the experience of living with a chronic pathology along with illness, disease and sickness dimensions emerged and were discussed. The study has pointed out the MINI narrative interview as a useful instrument to investigate the patients’ experiences suffering from a chronic illness and it provides a number of issues clinicians and medical professionals might integrate into the clinical practice when they use this narrative interview.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".