The use of McGill illness narrative interview (MINI) in fibromyalgia patients. An experience from Spain
Bibliographic record
Abstract
Introduction The McGill Illnes Narrative (MINI) is a semiestructured, qualitative interview schedule, which is useful to explore individuals’ illnes narratives in sociocultural context. It has been used in first- time postmyocardial infarction patients and also in patients with hyperemesis gravidarum, but it has not yet been used in fibromyalgia. Patients and methods A study was conducted using McGill Illness Narratives (MINI) with 20 patients who were recruited from a referral Rheumatology Service during 2009 and 2010, and met criteria of the American Association of Rheumatology for fibromyalgia syndrome (FMS) trying to explore: Narrative of illness experience Salient prototypes related to current health problem Explanatory models Help seeking and service utilization Impact of illness The interviews were carried out and audiorecorded and the narratives were analyzed according to their structure and content. Results The physical cause was the most common causal attribution. Most of them had been treated for different specialists, and reported problems in seeking diagnosis and help from professionals. Some patients were diagnosed by rheumatologists with celiac disease, lactose intolerance and undifferentiated spondylitis, termed “False Fibromyalgia”. Like previous research, negative emotional states were correlated with worsening pain. Many changes were related in their way of life. Conclusions Qualitative studies are essential to complement quantitative research methods and are imprescindible to understand FMS. The exploration of the explanatory models may stimulate exchange between disciplines and may give access to a popular cultural construct related to somatic conditions not yet documented in the literature.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".