‘When you preach water and you drink wine’: WASH in healthcare facilities in Kenya
Bibliographic record
Abstract
Abstract Access to basic water, sanitation and hygiene, waste management and environment cleaning (WASH) in healthcare facilities (HCFs) is critical for infection prevention and control. The WHO/UNICEF 2019 global baseline report on WASH in HCFs indicates that 51 and 23% of those in sub-Saharan Africa have basic access to water and sanitation, respectively. Guided by the political ecology of health theory, this research engaged with 13 key informants, 16 healthcare workers and 31 community members on their experiences on the implementation, use and management of WASH in HCFs. Interviews were conducted in one informal settlement and three rural dispensaries in Kisumu, Kenya from May to September 2019. Findings indicate improvement in water access, yet water quality and other WASH service components remain a challenge even in newly constructed maternity facilities, thus impacting local health promotion efforts. Institutional challenges such as limited financial resources and ecological factors like climate variability and disease outbreaks compromised WASH infrastructure and HCF resilience. To achieve Sustainable Development Goal 3, good health and well-being, as well as Sustainable Development Goal 6, clean water and sanitation, the prioritisation of WASH in HCFs is required at all levels, from the local to the global.
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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 | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".