<i>Maladi Nanm, Maladi Zonbi, & Maladi Lalin</i>: A qualitative study of cultural concepts of distress in northern Haiti
Bibliographic record
Abstract
Cultural Concepts of Distress (CCDs) are culturally constructed diagnostic categories that exist within a specific society or culture. While several studies have assessed CCDs around the world, few studies have examined them in Haiti. This qualitative study examines manifestations of anxiety and depression via “sent spirits” in the form of maladi nanm (“Soul disorder”) and maladi zonbi (“Zombie disorder”), and bipolar disorder in the form of maladi lalin (“Moon disorder”). Examples of CCDs were recorded as part of a study which interviewed 96 outpatients at the first mental health center in northern Haiti. Using qualitative methods, the authors identified three specific CCDs as reported by mental health patients. Maladi nanm and maladi zonbi represent alternative explanatory models of anxiety and depression in which the sufferer views mental illness as stemming from a sent spirit, or spirit which is intentionally sent supernaturally with the intent to cause harm. Maladi lalin is experienced by patients with bipolar disorder who associate cycles of mania and depression as in-sync with the phases of the moon. Understanding culture-bound forms of mental distress in settings such as Haiti is essential to developing accurate psychometrics for measuring mental health, as well as ensuring culturally appropriate and effective diagnosis and treatment.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".