Psychosocial impacts of the lack of access to water and sanitation in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
The lack of access to safe water and adequate sanitation has implications for the psychosocial well-being of individuals and households. To review the literature on psychosocial impacts, we completed a scoping review of the published literature using Medline, Embase, and Scopus. Fifteen studies met the inclusion criteria and were reviewed in detail. Of the included studies, six were conducted in India, one in Nepal, one in Mexico, one in Bolivia, two in Ethiopia, one in Zimbabwe, one in South Africa, and two in Kenya. Four interrelated groups of stressors emerged from the review: physical stressors, financial stressors, social stressors, and stressors related to (perceived) inequities. Further, gender differences were observed, with women carrying a disproportionate psychosocial burden. We argue that failure to incorporate psychosocial stressors when estimating the burden or benefits of safe water and sanitation may mask an important driver of health and well-being for many households in low- and middle-income countries. We propose further research on water-related stressors with particular attention to unique cultural norms around water and sanitation, short and long term psychosocial outcomes, and individual and collective coping strategies. These may help practitioners better understand cumulative impacts and mechanisms for addressing water and sanitation challenges.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".