Bibliographic record
Abstract
Indigenous communities are vulnerable to a variety of health risks due to political marginalization, socioeconomic challenges and geographic isolation. Most developed and developing nations rely mainly on biomedical healthcare services, which do not adequately incorporate the use of traditional medicinal knowledge. Peru is home to over 50 Indigenous groups, many of which practice holistic and traditional approaches to healthcare. Peruvian healers and medicinal plants play an integral role in such traditional medicinal systems. Integrative healthcare, which incorporates Indigenous medicine into the biomedical healthcare system, is a potential solution to improving healthcare services for an entire nation. However, integrative healthcare fails to address the lack of accessibility and affordability of the Peruvian healthcare system for marginalized populations. Traditional medicine reflects a multi-dimensional, spiritual and individualized approach to healthcare that is in conflict with the scientific and esoteric nature of the biomedical system. Incorporating traditional medicine into the biomedical system could threaten the existence of traditional medicinal knowledge and decrease the need for dissemination of traditional knowledge and culture. In a Peruvian context, integrative healthcare would have a detrimental impact on the maintenance and dissemination of Indigenous Peruvian medical knowledge. Keywords: Peru; Indigenous; health; policy; traditional, complementary and alternative medicine (TCAM)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".