Holy water and biomedicine: a descriptive study of active collaboration between religious traditional healers and biomedical psychiatry in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Religious and traditional healers remain the main providers of mental healthcare in much of Africa. Collaboration between biomedical and traditional treatment modalities is an underutilised approach, with potential to scale up mental healthcare. AIMS: To report the process and feasibility of establishing a collaboration between religious healers and psychiatrists in Addis Ababa, Ethiopia. To gain insight into the collaboration through studies of patient demographics, help-seeking patterns, nature of illness and receptivity of the project. METHOD: This case study describes the process and challenges in establishing a collaborative psychiatric clinic for patients who are simultaneously receiving treatment with holy water, including an examination of basic clinical records of 1888 patients over a 7-year period. RESULTS: The collaboration is feasible and has been successfully implemented for 8 years. A majority (54%) of the clinic's patients were seeing biomedical services for the first time. Patients were brought in largely by families (54%); 26% were referred directly by priest healers. Most patients had severe mental illness, including schizophrenia (40%), substance misuse (24%) and mood disorders (30%). A vast majority (92.2%) of patients reported comfort in receiving treatment with holy water and prayers simultaneously with medication, and 73.6% believed their illness was caused by evil spirit possession. CONCLUSIONS: A cross-system collaborative model is a feasible and potentially valuable model to address biomedical resource limitations. Provider collaboration and mutual learning are ultimately beneficial to patients with severe mental illness. Open-minded acceptance of cultural benefits and strengths of traditional healing is a prerequisite. Further study on outcomes and implementation are warranted.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".