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Record W7011737007

A Narrative Medicine Pilot Study Using the McGill Illness Narrative Interview (MINI) with Patients Suffering from Nephropathy and on Dialysis.

2019· article· en· W7011737007 on OpenAlexaboutno aff

Bibliographic record

VenueIRIS eCampus Telematic University (Università degli Studi eCampus) · 2019
Typearticle
Languageen
FieldMedicine
TopicGestational Trophoblastic Disease Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeNarrative medicineNarrative inquiryThematic analysisDiseaseClinical PracticeQualitative researchAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.240
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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