An Application of the Integrated Behavioral Model for Water, Sanitation and Hygiene to Assess Perceived Community Acceptability and Feasibility of the Biosand Filter among Maasai Pastoralists in Rural Tanzania
Bibliographic record
Abstract
In addition to diarrheal disease risk, lack of access to safe water may have other indirect effects throughout one's life, such as school and workplace absenteeism, leading to less economic productivity. In contexts with scarce resources and unsafe drinking water, household water treatment and safe storage options such as the Biosand filter (BSF) allows households to directly reduce contamination and increase the quality of their drinking water. This study aimed to develop an understanding of perceived community acceptability and feasibility related to pre- and post-implementation of a BSF pilot project in rural Maasai households in the Ngorongoro Conservation Area (NCA), Tanzania. The study was guided by the Integrated Behavioral Model for Water Sanitation and Hygiene interventions (IBM-WASH) to understand the various factors influencing end-user perceptions of the BSF. In-depth interviews, group discussions and think tanks were conducted among a cross-section of community members, stakeholders, and other actors from May 2016 to September 2017. The data were analyzed using a thematic content analysis approach. A range of perceived contextual, technological, and psychosocial factors were found to potentially affect the acceptability and feasibility of BSF adoption in the NCA, highlighting the complex layers of influences in the setting. Whilst the BSF is seemingly an accepted option to treat water within the NCA, the community identified key barriers that may lower BSF adoption. The application of the IBM-WASH model served as a useful framework for evaluating the introduction of the BSF, identifying insights into contextual, technological, and psychosocial community factors.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".