Indigenous Traditional Medicine and Intercultural Healthcare in Bolivia: A Case Study From the Potosi Region
Bibliographic record
Abstract
Indigenous peoples have the worst socio-demographic indicators and the largest inequalities in terms of access to social services and health in the Latin American region, Bolivia included. In the last few years, attempts to implement policies that support indigenous people's health rights led to the development of intercultural health approaches. Yet, acceptance and integration of indigenous medicine into the biomedical health system presents a major challenge to intercultural health in Latin America. The objective of this article is to analyze the case of a health center in Tinguipaya, one of the first and few examples of intercultural health initiatives in Bolivia. This intercultural health project, which represents a pioneer experience with regard to the creation of intercultural health services in Bolivia, aims to create a network between local communities, traditional healers, and biomedical staff and offer a more culturally sensitive and holistic health service for indigenous people living in the area. The aim of this article is to critically assess this initiative and to analyze the main challenges met in the creation of a more effective intercultural health policy. The extent to which this initiative succeeded in promoting the integration between indigenous health practitioners and biomedical staff as well as in improving access to health care for local indigenous patients will also be examined.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".