The McGill Illness Narrative Interview (MINI): An Interview Schedule to Elicit Meanings and Modes of Reasoning Related to Illness Experience
Bibliographic record
Abstract
This article summarizes the rationale, development and application of the McGill Illness Narrative Interview (MINI), a theoretically driven, semistructured, qualitative interview protocol designed to elicit illness narratives in health research. The MINI is sequentially structured with three main sections that obtain: (1) A basic temporal narrative of symptom and illness experience, organized in terms of the contiguity of events; (2) salient prototypes related to current health problems, based on previous experience of the interviewee, family members or friends, and mass media or other popular representations; and (3) any explanatory models, including labels, causal attributions, expectations for treatment, course and outcome. Supplementary sections of the MINI explore help seeking and pathways to care, treatment experience, adherence and impact of the illness on identity, self-perception and relationships with others. Narratives produced by the MINI can be used with a wide variety of interpretive strategies drawn from medical anthropology, sociology and discursive psychology.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".