Maasai women hearing voices: Implications for global mental health
Bibliographic record
Abstract
There is a sparse literature on women who hear voices globally, even though there are documented gendered dimensions of distress in the context of globalization and climate change and research indicates that trauma and psychosocial stress may be related to an increased prevalence of voice-hearing or auditory verbal hallucinations (AVHs). There is also a gap in the cultural phenomenology of voice-hearing in general, as well as idioms of distress for non-western peoples. This article presents results of a mixed methods study that: 1) estimated community prevalence of voice-hearing among Maasai women in northern Tanzania; 2) examined any demographic correlates and two specific hypothesized correlates (i.e., psychological stress and potentially traumatic events); and 3) engaged women in semi-structured interviews about their everyday lives and the phenomenological experience of voice-hearing. The prevalence of voice-hearing (39.4%) in this nonclinical sample (n = 71) was quite high compared to other studies in sub-Saharan Africa. Most women also reported high psychosocial stress and traumatic life events. They also talked about gendered conditions of social adversity in a context of rapid social, economic, and climate change. Women who reported hearing voices had a statistically significantly higher level of psychological distress, met criteria for severe psychological distress, and reported more potentially traumatic life events. In a logistic regression model, psychosocial stress predicted voice-hearing. The presence of distressing voices may offer a straightforward way to quickly identify people in the community experiencing the most extreme levels of psychosocial stress and traumatic events-a potentially simple but effective screening tool for health workers on the ground.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".