Reconsidering the Allure of the Culturally Distant in Therapy Seeking: A Case Study from Coastal Tanzania
Bibliographic record
Abstract
This article examines two seemingly contradictory notions found in the anthropological literature that address so-called traditional healers. First, it suggests that despite their purportedly holistic approach, healers in coastal Tanzania may not be as popularly sought after by "local" people as they are made out to be by some academics and health policy researchers. Second, it contends that although there may be a tendency among the people of Tanzania to consult "distant" healers for social relationship-related conditions, the decision-making process involved in seeking out such healers is far more dynamic and context dependent than has been previously reported in the literature. People who seek help from distant healers have often unsuccessfully tried locally available health care resources. In making these arguments, I draw on ethnographic data gathered in a large village in the Dar es Salaam region of coastal Tanzania. In particular, I examine the divinatory practices of a well-known Zaramo healer (mganga) and discuss narrative case studies of two patients who had traveled from distant places to seek the mganga's help. The article concludes with a call for the critical reevaluation of propositions for the integration of "traditional healers" in programs aimed at the prevention and treatment of life-threatening infectious diseases that are predicated mainly on the assumption that healers are popular among the local people and provide effective consultations.
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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 | 0.003 | 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 | 0.019 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".