Water (In)Accessibility, Healthcare Delivery, and Patients’ Health Outcomes in Ghana: Perspectives from the Yendi Hospital
Bibliographic record
Abstract
Background: Access to water, sanitation, and hygiene (WASH) services is internationally recognized as a fundamental human right and an essential determinant of health. Yet, many healthcare facilities in sub-Saharan Africa face persistent WASH deficits, undermining safe and effective care delivery. Aim: To explore how water (in)accessibility influences patient healthcare experiences and patient–provider relationships in Yendi Hospital, a major referral facility in northern Ghana. Methods: Using a qualitative design, we gathered data from patients (n = 21), caregivers (n = 11), and nurses (n = 11) through in-depth interviews, participant observation, and a focus group to document their lived experiences and perceptions. We transcribed and inductively coded the data for thematic analysis. Results: Our key findings reveal that water inaccessibility is not solely an infrastructural issue but also a pervasive challenge with profound implications for care delivery. Patients and caregivers often leave the hospital to bathe at home, resulting in missed ward rounds, delayed reviews, and/or refusal of admission. Nurses described how water inaccessibility disrupted clinical routines and strained relationships with patients and caregivers. These dynamics eroded trust, rapport, and professional morale, while exacerbating inequities in healthcare access and outcomes. Conclusions: This study underscores that addressing water challenges in the hospital is imperative not only for infection control but also for fostering equity, patient rights, and institutional resilience. We argue that policy interventions to strengthen WASH systems are urgently required to advance progress toward Sustainable Development Goal 6.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".