Navigating water and sanitation environments in schools: Exploring health risk perceptions of children with physical disabilities using drawing
Bibliographic record
Abstract
• The UN Sustainable Development Goals prioritize healthy water and sanitation for everyone. • The study used art-based methodologies to examine how children with physical disabilities navigate the WASH environment in primary schools in Ghana, as well as the perceived health concerns attached to their poor access. • Poor WASH access by persons with physical disabilities can lead to dehydration resulting from low water intake, diarrhea, vulnerability to abuse, and various forms of psychosocial stress. • Integrating WASH interventions in schools can have a positive impact on attendance, reduce absenteeism, improve health, and reduce the learning gap for disadvantaged children. Ensuring universal access to safe water and sanitation remains a central goal of the United Nations Sustainable Development Goals (SDGs). However, persons with physical disabilities continue to encounter numerous barriers—stemming from capacity, environmental, and personal constraints—when accessing these facilities. This study aimed to (i) explore how children with physical disabilities navigated water, sanitation, and hygiene (WASH) environments in primary schools in Ghana and (ii) investigate their perceived health risks associated with these environments. Children were given prompts to draw and write about their school WASH contexts, followed by interviews to discuss their drawings. A thematic analysis of their narratives and artwork revealed several barriers, including physically inaccessible facilities, poor maintenance, and limited peer support or mobility aids. Commonly reported health implications included dehydration from inadequate water intake, diarrhoea, increased vulnerability to abuse, and psychosocial stress. The findings further showed that the type and severity of disability influenced the extent of these challenges. To advance SDGs 6, 4, and 3, strategies must be implemented to create safe, inclusive school environments that address the diverse needs of all learners.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".