Achieving the Sustainable Development Goals: A Mixed Methods Study of Health-Related Water, Sanitation, and Hygiene (WASH) for Indigenous Shawi in the Peruvian Amazon
Bibliographic record
Abstract
Sustainable Development Goal 6 (SDG-6) addresses poor water quality, inadequate sanitation, and improper hygiene, all of which negatively impact health and disproportionately impact Indigenous Peoples' health. Understanding and responding to local contexts is critical to effectively improve water, sanitation, and hygiene (WASH); however, in-depth understanding of local knowledge, practices, and perceptions are often overlooked. As such, this study described the knowledge, practices, and perceptions of WASH held by residents of two Indigenous Shawi communities in the Peruvian Amazon. Quantitative data were collected via a cross-sectional survey and analyzed using descriptive statistics. Qualitative data were collected via interviews, PhotoVoice, focus group discussions, and participatory transect walks, and analyzed using a constant comparative approach to thematic analysis. Emergent themes included characterizing water sources, collection methods, and consumption patterns; knowledge, perceptions, and practices related to WASH; and knowledge and perceptions of health issues related to WASH. This study provides insight into the ongoing challenges related to WASH in Indigenous communities in the Peruvian Amazon and highlights the need to prioritize interventions that will advance WASH-related SDGs.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".