Water Scarcity and Water Quality: Identifying Potential Unintended Harms and Mitigation Strategies in the Implementation of the Biosand Filter in Rural Tanzania
Bibliographic record
Abstract
Bottom-up public health interventions are needed which are built on an understanding of community perspectives. Project SHINE is a community-based participatory action research intervention focused on developing sustainable water, sanitation, and hygiene strategies with Maasai pastoralists in Tanzania. The aim of the study is to understand perceptions related to water quality and scarcity as well as to assess the potential of the Biosand Filter as a low-cost, low-tech water treatment option. To avoid unintended harms, the community was engaged in identifying potential harms and mitigation strategies prior to the implementation of the filter.Two in-depth interviews and two group discussions were analyzed using thematic content analysis, while three think tanks were analyzed using directed content analysis. The findings highlight a range of concerns regarding water scarcity and quality. The think tank approach was an effective means of engaging the community in identifying potential unintended harms across four dimensions: the physical, psychosocial, economic, and cultural contexts. In addition, two external themes emerged as salient: political harm and harm by omission.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".