“Trust in God, but tie your donkey”: Holy water priest healers’ views on collaboration with biomedical mental health services in Addis Ababa, Ethiopia
Bibliographic record
Abstract
This exploratory qualitative study examines holy water priest healers' explanatory models and general treatment approaches toward mental illness, and their views and reflections on a collaborative project between them and biomedical practitioners. The study took place at two holy water treatment sites in Addis Ababa, Ethiopia. Twelve semi-structured interviews with holy water priest healers found eight notable themes: they held multiple explanatory models of illness, dominated by religious and spiritual understanding; they emphasized spiritual healing and empathic understanding in treatment, and also embraced biomedicine as part of an eclectic healing model; they perceived biomedical practitioners' humility and respect as key to their positive views on the collaboration; they valued recognition of their current role and contribution in providing mental healthcare; they recognized and appreciated the biomedical clinic's effectiveness in treating violent and aggressive patients; they endorsed the collaboration and helped to overcome patient and family reluctance to the use of biomedicine; they lamented the lack of spiritual healing in biomedical treatment; and they had a number of dissatisfactions and concerns, particularly the one-way referral from religious healers to the biomedical clinic. The study results show diversity in the religious healers' etiological understanding, treatment approaches and generally positive attitude and views on the collaboration. We present insights and explorations of factors affecting this rare, but much needed collaboration between traditional healers and biomedical services, and potential ways to improve it are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".