Taking the MINI to Mustang, Nepal: methodological and epistemological translations of an illness narrative interview tool
Bibliographic record
Abstract
Illness narratives and explanatory models have been a research focus for the discipline of medical anthropology for decades. In recent years, standardized qualitative research tools have been developed to elicit illness narratives as a means of conducting socio-cultural analysis and as a springboard for health-related interventions - particularly with reference to communities experiencing rapid socioeconomic transition or those in which trauma has been experienced. Nevertheless, gaps persist in terms of the latent methodological and epistemological challenges of translating and transplanting such research tools to new contexts. This paper chronicles the adaptation of the McGill Illness Narrative Interview (MINI) for use in the culturally Tibetan region of Mustang, Nepal. This analysis is based on 44 in-depth interviews using an adapted version of the MINI to elicit narratives about experiences of illness. The MINI proved to be a compelling research tool, particularly in terms of engaging research assistants in the field. Yet its deployment in a context where distinctions between individual and social suffering can be blurred, where the dichotomization of 'religion' and 'medicine' makes little sense, and where understandings of causality are rooted in the concept of karma, revealed the extent to which the MINI - and, by extension other such qualitative research tools - emerges from particular models of narrative construction and assumptions about the relationships between self and other, cause and effect. Concluding recommendations are made regarding the adaptation of this tool to other settings.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".