Searching for people with psychosis in the global south: mapping and establishing a case surveillance system in South Africa (PSYMAP-ZN study)
Bibliographic record
Abstract
PURPOSE: Relatively little epidemiological evidence on psychosis from diverse settings in the Global South exists, where many people with untreated psychosis seek help outside of formal health service settings. Here, we report a preliminary mapping study of formal and informal community resources within a catchment area in South Africa that established an infrastructure that could be used to detect a representative sample of individuals with untreated psychosis. METHODS: PSYMAP-ZN is a 3-year study of incidence, clinical presentation and associated risk factors for untreated psychosis in Msunduzi Municipality in South Africa. We conducted a preliminary mapping study of the region in which we aimed to document all potential providers of care (gatekeepers) in both formal (health services) and informal (folk) sectors, with the purpose of enrolling them in a collaborative case surveillance system. We drew on official sources, local knowledge from key stakeholders and utilised snowballing techniques. RESULTS: We established a surveillance system which included (a) all secondary mental health and primary care services (b) the majority of informal providers (including traditional health practitioners, religious institutions) and (c) a wide range of key informants. CONCLUSION: Expanding the global knowledge base on psychosis to diverse settings in the Global South requires a surveillance and case-detection method that includes (in addition to formal health settings) informal settings and local key informant knowledge in the community. This preliminary 'mapping' process established a platform for the ongoing PSYMAP study of untreated psychosis in South Africa.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".