“Blood is thicker than water” - experiences and perspectives of family caregivers of people living with severe mental illness at Holy Water traditional healing sites, Addis Ababa, Ethiopia
Bibliographic record
Abstract
Background: Family caregivers of people with severe mental illness (SMI) are the backbone of the mental health care system in resource-limited family centered cultural setting like Ethiopia. This exploratory qualitative study examines the experiences and perspectives of family caregivers at two Ethiopian Holy Water treatment sites for people with SMI in Addis Ababa, where a collaborative project exists between traditional healers and biomedical practitioners. Methods: Eleven family caregivers at two Holy Water treatment sites in Addis Ababa, Ethiopia were interviewed in 2021, using a semi-structured interview guide. The transcribed material was analyzed using qualitative thematic analysis. Inclusion criteria for study participants were over 18 years of age, capacity to give informed consent, and has a family member with mental illness for whom the family caregiver has sought help at the Holy Water treatment sites and at the collaborative Clinic. Results: Content analysis found seven notable themes: 1. Strong sense of obligation and responsibility and ongoing provision of care; 2. Caregiving puts a serious strain on caregivers' lives and established family roles; 3. Chronicity and persistence of illness take toll on family caregivers and networks of support; 4. Family caregivers appreciate the supportive religious setting and attendants at Holy Water treatment community; 5. Family caregivers develop a community of mutual support for each other; 6. Severe shortage and poor access to formal biomedical services and appreciation of the collaborative Clinic; 7. Burden, exhaustion, and loss of hope regarding the future. Conclusion: The study shows that families in Ethiopia face a protracted and heavy caregiver burden in their caretaking duties, often in isolation, with a severe lack of formal biological treatment and psychosocial support. Informal assistance and mutual support form part of the culturally shaped support networks, but there are on-going challenges. Innovative programs with collaborative approach show some promise. More development of community mental health services and support are urgently needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".