“Head take you”: Causal attributions of mental illness in Jamaica
Bibliographic record
Abstract
Causal attributions are a key factor in explanatory models of illness; however, little research on causal attributions of mental illness has been conducted in developing nations in the Caribbean, including Jamaica. Explanatory models of mental illness may be important in understanding illness experience and be a crucial factor in mental health service seeking and utilization. We explored causal attributions of mental illness in Jamaica by conducting 20 focus groups, including 16 community samples, 2 patient samples, and 2 samples of caregivers of patients, with a total of 159 participants. The 5 most commonly endorsed causal attributions of mental illness are discussed: (a) drug-related causes, including ganja (marijuana); (b) biological causes, such as chemical imbalance, familial transmission, and "blood"; (c) psychological causes, including stress and thinking too much; (d) social causes, such as relationship problems and job loss; and (e) spiritual or religious causes, including Obeah.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".