Optimizing household survey methods to monitor the Sustainable Development Goals targets 6.1 and 6.2 on drinking water, sanitation and hygiene: A mixed-methods field-test in Belize
Bibliographic record
Abstract
BACKGROUND: The Sustainable Development Goals (SDGs) require household survey programmes such as the UNICEF-supported Multiple Indicator Cluster Surveys (MICS) to enhance data collection to cover new indicators. This study aims to evaluated methods for assessing water quality, water availability, emptying of sanitation facilities, menstrual hygiene management and the acceptability of water quality testing in households which are key to monitoring SDG targets 6.1 and 6.2 on drinking Water, Sanitation and Hygiene (WASH) and emerging issues. METHODS: As part of a MICS field test, we interviewed 429 households and 267 women age 15-49 in Stann Creek, Belize in a split-sample experiment. In a concurrent qualitative component, we conducted focus groups with interviewers and cognitive interviews with respondents during and immediately following questionnaire administration in the field to explore their question comprehension and response processes. FINDINGS: About 88% of respondents agreed to water quality testing but also desired test results, given the potential implications for their own health. Escherichia coli was present in 36% of drinking water collected at the source, and in 47% of samples consumed in the household. Both questions on water availability necessitated probing by interviewers. About one quarter of households reported emptying of pit latrines and septic tanks, though one-quarter could not provide an answer to the question. Asking questions on menstrual hygiene was acceptable to respondents, but required some clarification and probing. CONCLUSIONS: In the context of Belize, this study confirmed the feasibility of collecting information on the availability and quality of drinking water, emptying of sanitation facilities and menstrual hygiene in a multi-purpose household survey, indicating specific areas to improve question formulation and field protocols. Improvements have been incorporated into the latest round of MICS surveys which will be a major source of national data for monitoring of SDG targets for drinking water, sanitation and hygiene and emerging issues for WASH sector programming.
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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 | 0.073 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".