Experiences and perspectives on traditional and faith healers’ involvement in the care of people with severe mental health conditions in ethiopia: a scoping review
Bibliographic record
Abstract
BACKGROUND: Traditional and faith healers (TFHs) play a prominent role in the care of people with severe mental health conditions (MHCs) in many countries. Consequently, there have been calls for closer collaboration between TFHs and mental health care practitioners. This scoping review aimed to map the literature on the experiences of, and perspectives on, traditional and faith healing for people with severe MHCs in Ethiopia. METHODS: The review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews. Pubmed, Embase, CINAHL, Scopus, Web of Science, and PsycINFO databases were searched from the earliest available records to May 2024. Online student MSc/PhD theses and catalogued Ethiopian publications up to 2015 were also searched. Studies were included if they were in English and of any study design using primary data collection. Narrative synthesis was chosen for data synthesis. RESULTS: Of the 3,824 records identified, 31 were included. There were 17 qualitative, 12 quantitative, and two mixed methods studies, conducted in most regions in Ethiopia but with more focus on urban than rural settings. Findings were synthesised under the following themes: perceived causes of MHCs; pathways to care and help-seeking preferences; identification and intervention methods used by TFHs; experience of treatment, satisfaction with care, gaps, and barriers; and collaboration between TFHs and mental health practitioners. People with severe MHCs commonly accessed TFHs first and alongside biomedical care. A substantial range of healers was identified but they were not accessible or acceptable to all communities equally. TFH interventions were diverse and some of their practices were reported to be harmful. However, there were few in-depth studies of TFH care processes. Furthermore, there was little evidence about the experience of care from the perspective of people with severe MHCs. Efforts toward collaboration emphasised the need to develop relationships within which differences could be negotiated. CONCLUSION: Although much is known about the place of TFHs within care pathways for people with MHCs in Ethiopia, there are evidence gaps in relation to the perspectives of people with MHCs and rich contextual understanding of healing processes, both of which are needed for meaningful collaboration to occur.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".