Attitudes towards african traditional medicine and christian spiritual healing regarding treatment of epilepsy in a rural community of northern Tanzania
Bibliographic record
Abstract
Most people with epilepsy (PWE) live in developing countries with limited access to health care facilities. In sub-Saharan Africa with approximately 12 million PWE, 90% do not receive adequate medical treatment. In this context, traditional medicine, being easily accessible, plays an important role. However, in sub- Saharan Africa, studies on the attitude of people (both affected and not affected by epilepsy) towards traditional medicine for treatment of epilepsy are scarce. In this study, 167 people (59 PWE, 62 relatives, 46 villagers) were interviewed at the hospital and in the community with a semi-structured validated questionnaire regarding the prevailing attitude towards traditional medicine for treatment of epilepsy in a rural area of northern Tanzania. Various traditional healing methods (THM) could be ascertained, i.e. traditional herbal medicine, spiritual healing, scarifications and spitting. 44.3% (n=74/167) of the interviewed people were convinced that epilepsy could be treated successfully with THM. Interestingly, 34.1% (n=57/167) thought that Christian prayers could cure the cause and/or treat symptoms of epilepsy. Significantly more PWE and their relatives were in favour of THM compared to villagers not knowing about epilepsy or not being immediately affected by epilepsy (χ(2)-test, p=0.004). Further factors influencing people's attitudes towards THM were gender, tribe, religion and urbanity of people's dwellings. Our study demonstrates that not only THM but also prayers in the Christian sense seem to play an important role in people's beliefs regarding successful treatment of epilepsy. Factors influencing this belief system have been identified and are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".