Human African Trypanosomiasis: Ethnomedical and Biomedical Relationships
Bibliographic record
Abstract
Human African Trypanosomiasis (HAT) threatens more than 55 million people in sub-Saharan Africa. The focus of this paper is the relationship that exists between ethnomedical and biomedical practitioners regarding treatment of HAT. The relationship has been one of conflict. Biomedical practitioners have attempted to remove ethnomedicine from African society. This practice has been unsuccessful, especially in rural regions. Ethnomedical practitioners have adopted some aspects of biomedicine, but inadequate application and resources limit their efficacy. The biomedical community, including the World Health Organization, has not recognized the resource potential within ethnomedicine. An examination of the areas of conflict reveals differences in the explanatory models of disease causation and treatment methods. There has been some progress toward a cooperative model of healthcare, but most biomedical practitioners disregard ethnomedical techniques as primitive and ineffective. The conclusion of this paper presents a bleak future for the prevention and treatment of HAT in Africa. This future may be avoided with an increased level of cooperation and understanding between the two systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".