A qualitative study of barriers and facilitators to adequate environmental health conditions and infection control for healthcare workers in Malawi
Bibliographic record
Abstract
Abstract The burden of healthcare-associated infections (HAIs) is high in low- and middle-income countries. Inadequate environmental health (EH) conditions and work systems contribute to HAIs in countries like Malawi. We collected qualitative data from 48 semi-structured interviews with healthcare workers (HCWs) from 45 healthcare facilities (HCFs) across Malawi and conducted a thematic analysis. The facilitators of infection prevention and control (IPC) practices in HCFs included disinfection practices, patient education, and waste management procedures. HCWs reported barriers such as lack of IPC training, bottlenecks in maintenance and repair, hand hygiene infrastructure, water provision, and personal protective equipment. This is one of the most comprehensive assessments to date of IPC practices and environmental conditions in Malawian HCFs in relation to HCWs. A comprehensive understanding of barriers and facilitators to IPC practices will help decision-makers craft better interventions and policies to support HCWs to protect themselves and their patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".