Health facilities readiness for standard precautions to infection prevention and control in Nepal: A secondary analysis of Nepal Health Facility Survey 2021
Bibliographic record
Abstract
BACKGROUND: Improvements in standard precaution related to infection prevention and control (IPC) at the national and local-level health facilities (HFs) are critical to ensuring patient's safety, preventing healthcare-associated infections (HAIs), mitigating Antimicrobial Resistance (AMR), protecting health workers, and improving trust in HFs. This study aimed to assess HF's readiness to implement standard precautions for IPC in Nepal. METHODS: This study conducted a secondary analysis of the nationally-representative Nepal Health Facility Survey (NHFS) 2021 data and used the Service Availability and Readiness Assessment (SARA) Manual from the World Health Organization (WHO) to examine the HF's readiness to implement standard precautions for IPC. The readiness score for IPC was calculated for eight service delivery domains based on the availability of eight tracer items: guidelines for standard precautions, latex gloves, soap and running water or alcohol-based hand rub, single use of standard disposal or auto-disable syringes, disinfectant, safe final disposal of sharps, safe final disposal of infectious wastes, and appropriate storage of infectious waste. We used simple and multiple linear regression and quantile regression models to examine the association of HF's readiness with their characteristics. Results were presented as beta (β) coefficients and 95% confidence interval (95% CI). RESULTS: The overall readiness scores of all HFs, federal/provincial hospitals, local HFs, and private hospitals were 59.9±15.6, 67.1±14.4, 59.6±15.6, and 62.6±15.5, respectively. Across all eight health service delivery domains, the HFs' readiness for tuberculosis services was the lowest (57.8±20.0) and highest for delivery and newborn care services (67.1±15.6). The HFs performing quality assurance activities (β = 3.68; 95%CI: 1.84, 5.51), reviewing clients' opinions (β = 6.66; 95%CI: 2.54, 10.77), and HFs with a monthly meeting (β = 3.28; 95%CI: 1.08, 5.49) had higher readiness scores. The HFs from Bagmati, Gandaki, Lumbini, Karnali and Sudurpaschim had readiness scores higher by 7.80 (95%CI: 5.24, 10.36), 7.73 (95%CI: 4.83, 10.62), 4.76 (95%CI: 2.00, 7.52), 9.40 (95%CI: 6.11, 12.68), and 3.77 (95%CI: 0.81, 6.74) compared to Koshi. CONCLUSION: The readiness of HFs to implement standard precautions was higher in HFs with quality assurance activities, monthly HF meetings, and mechanisms for reviewing clients' opinions. Emphasizing quality assurance activities, implementing client feedback mechanisms, and promoting effective management practices in HFs with poor readiness can help to enhance IPC efforts.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".