Acceptability measures of water, sanitation and hygiene interventions in low- and middle-income countries, a systematic review
Bibliographic record
Abstract
BACKGROUND: Inadequate access to water, sanitation, and hygiene (WASH) is an environmental risk factor for poor health outcomes globally, particularly for children in low- and middle-income countries (LMIC). Despite technological advancements, many interventions aimed at improving WASH access return less than optimal results on long term impact, efficacy and sustainability. Research focus in the 'WASH sector' has recently expanded from investigating 'which interventions work' to 'how they are best implemented'. The 'acceptability' of an intervention is a key component of implementation that can influence initial uptake and sustained use. Acceptability assessments are increasingly common for health interventions in clinical settings. A broad scale assessment of how acceptability has been measured in the WASH sector, however, has not yet been conducted. METHODS/PRINCIPAL FINDINGS: We conducted a systematic literature review of intervention studies published between 1990 and 2021 that evaluated the acceptability of WASH interventions in LMIC settings. Using an implementation science approach, focused outcomes included how acceptability was measured and defined, and the timing of acceptability assessment. We conducted quality assessment for all included studies using the Cochrane Risk of Bias tool for randomised studies, and the Newcastle-Ottawa Scale for non-randomised studies. Of the 1238 records; 36 studies were included for the analysis, 22 of which were non-randomized interventions and 16 randomized or cluster-randomized trials. We found that among the 36 studies, four explicitly defined their acceptability measure, and six used a behavioural framework to inform their acceptability study design. There were few acceptability evaluations in schools and healthcare facilities. While all studies reported measuring WASH acceptability, the measures were often not comparable or described. CONCLUSIONS: As focus in WASH research shifts towards implementation, a consistent approach to including, defining, and measuring acceptability is needed.
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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 | 0.051 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".