Disinfection of Neonatal Resuscitation Equipment in Resource-Limited Settings: Lessons From a Mixed-Methods Implementation Experience in Kenya
Bibliographic record
Abstract
BACKGROUND: The majority of neonatal deaths occur in low- and middle-income countries, most often due to perinatal events, prematurity, and/or infection. Reprocessing of neonatal resuscitation equipment is vital for ensuring the availability of clean equipment and preventing transmission of infection to a newborn. Staff at Tenwek Hospital, a tertiary referral hospital in rural Kenya, identified reprocessing medical equipment as a gap in improving neonatal care. We sought to implement steam-based high-level disinfection (HLD) for reprocessing neonatal resuscitation equipment in the labor and delivery ward of Tenwek Hospital. NEEDS ASSESSMENT: Before implementation, a needs assessment was conducted to identify existing facilitators and barriers to reprocessing through semistructured interviews with key stakeholders at the hospital (N=12) and identify gaps in the hospital's existing reprocessing procedures. A chemical, chlorine-based method of disinfection was used for neonatal resuscitation equipment in the ward. We conducted baseline bacterial burden of neonatal resuscitation equipment before clinical use, after clinical use, and after reprocessing. There was not a significant decrease in bacterial burden after reprocessing. IMPLEMENTATION: After implementing a new steam-based HLD process, we conducted bacterial burden testing, which showed a reduction. However, staff preferences and implementation challenges compelled us to modify our original plan and instead implement optimized chemical HLD using chlorine. Although testing showed improved bacterial burden from baseline, in our small number of samples, bacterial burden testing after implementing the optimized chemical HLD process did not differ significantly compared to steam-based HLD. CONCLUSIONS: Optimal chemical HLD was felt to be feasible and sustainable in the local setting. Reprocessing methods should be designed for unique challenges in low-resource settings.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".