The epidemiology of ‘bewitchment’ as a lay-reported cause of death in rural South Africa
Bibliographic record
Abstract
BACKGROUND: Cases of premature death in Africa may be attributed to witchcraft. In such settings, medical registration of causes of death is rare. To fill this gap, verbal autopsy (VA) methods record signs and symptoms of the deceased before death as well as lay opinion regarding the cause of death; this information is then interpreted to derive a medical cause of death. In the Agincourt Health and Demographic Surveillance Site, South Africa, around 6% of deaths are believed to be due to 'bewitchment' by VA respondents. METHODS: Using 6874 deaths from the Agincourt Health and Socio-Demographic Surveillance System, the epidemiology of deaths reported as bewitchment was explored, and using medical causes of death derived from VA, the association between perceptions of witchcraft and biomedical causes of death was investigated. RESULTS: The odds of having one's death reported as being due to bewitchment is significantly higher in children and reproductive-aged women (but not in men) than in older adults. Similarly, sudden deaths or those following an acute illness, deaths occurring before 2001 and those where traditional healthcare was sought are more likely to be reported as being due to bewitchment. Compared with all other deaths, deaths due to external causes are significantly less likely to be attributed to bewitchment, while maternal deaths are significantly more likely to be. CONCLUSIONS: Understanding how societies interpret the essential factors that affect their health and how health seeking is influenced by local notions and perceived aetiologies of illness and death could better inform sustainable interventions and health promotion efforts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".