Kaniuwatewara (when we get sick): understanding health-seeking behaviours among the Shawi of the Peruvian Amazon
Bibliographic record
Abstract
BACKGROUND: Detailed qualitative information regarding Indigenous populations' health-seeking behaviours within Peru's plural healthcare system is lacking. Such context-specific information is prerequisite to developing evidence-based health policies and programs intended to improve health outcomes for Indigenous populations. To this end, this study aimed to characterize health-seeking behaviours, factors affecting health-seeking behaviours, and barriers to obtaining healthcare in two Indigenous Shawi communities in Peru. METHODS: Community-based approaches guided this work, and included 40 semi-structured interviews and a series of informal interviews. Data were analysed thematically, using a constant comparative method; result authenticity and validity were ensured via team debriefing, member checking, and community validation. RESULTS: Shawi health-seeking behaviours were plural, dynamic, and informed by several factors, including illness type, perceived aetiology, perceived severity, and treatment characteristics. Traditional remedies were preferred over professional biomedical healthcare; however, the two systems were viewed as complementary, and professional biomedical healthcare was sought for illnesses for which no traditional remedies existed. Barriers impeding healthcare use included distance to healthcare facilities, costs, language barriers, and cultural insensitivity amongst professional biomedical practitioners. Nevertheless, these barriers were considered within a complex decision-making process, and could be overridden by certain factors including perceived quality or effectiveness of care. CONCLUSIONS: These findings emphasize the importance of acknowledging and considering Indigenous culture and beliefs, as well as the existing traditional medical system, within the professional healthcare system. Cultural competency training and formally integrating traditional healthcare into the official healthcare system are promising strategies to increase healthcare service use, and therefore health outcomes.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".