Necessary conditions for sustainable water and sanitation service delivery in schools: A systematic review
Bibliographic record
Abstract
Access to water, sanitation, and hygiene (WASH) services confers significant health and economic benefits, especially for children, but only if those services can be delivered on a consistent basis. The challenge of sustainable, school-based WASH service delivery has been widely documented, particularly in resource-constrained contexts. We conducted a systematic review of published research that identifies drivers of, or tests solutions to, this challenge within low- and middle-income countries (PROSPERO 2020 CRD42020199163). Authors in the first group employ cross-sectional research designs and interrogate previously implemented school WASH interventions. Most conclude that dysfunctional accountability and information sharing mechanisms drive school WASH service delivery failures. By contrast, most of the interventions developed and tested experimentally by authors in the second group focus on increasing the financial and material resources available to schools for WASH service delivery. Overall, these authors find negligible impact of such infusions of cash, infrastructure, and supplies across a variety of sustainability outcome metrics. Taken together, the evidence suggests that sustainable service delivery depends on three simultaneously necessary components: resources, information, and accountability. Drawing upon theory and evidence from social psychology, public management, and political science, we identify priority knowledge gaps that can meaningfully improve the design of effective interventions. We also highlight the importance of both interdisciplinary collaboration and local expertise in designing WASH programming that aligns with sociocultural and institutional norms, and is thus more likely to generate sustainable impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".