Beliefs and perception of ill-health causation: a socio-cultural qualitative study in rural North-Eastern Ethiopia
Bibliographic record
Abstract
BACKGROUND: Understanding perceptions of the causes of ill-health common in indigenous communities may help policy makers to design effective integrated primary health care strategies to serve these communities. This study explored the indigenous beliefs of ill-health causation among those living in the Tehuledere Woreda /district/ in North East Ethiopia from a socio-cultural perspective. METHODS: The study employed a qualitative ethnographic method informed by Murdock's Theory of Illness. Participatory observation, over a total of 5 months during the span of one year, was supplemented by focus group discussions (n = 96 participants in 10 groups) and in-depth interviews (n = 20) conducted with key informants. Data were analyzed thematically using narrative strategies. RESULTS: In these communities, illness is perceived to have supernatural (e.g., almighty God/ Allah, nature spirits, and human agents of the supernatural), natural (e.g., environmental sanitation and personal hygiene, poverty, biological and psychological factors) and societal causes (e.g., social trust, experiences of family support and harmony; and violation of social taboos). Therefore, the explanatory model of illness causation in this community was very similar to that of the Murdock model with one key difference: social elements need to be added to the model. CONCLUSION: Members of the study community believes that supernatural, natural and social elements are linked to ill-health causation. A successful integrated primary health care strategy should include strategies for supporting patients' needs in all three of these domains.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".