Implementation of the infection prevention and control core components at the national level: a global situational analysis
Bibliographic record
Abstract
BACKGROUND: Strengthening infection prevention and control (IPC) is essential to combat healthcare-associated infections, antimicrobial resistance, and to prevent and respond to outbreaks. AIM: To assess national IPC programmes worldwide according to the World Health Organization (WHO) IPC core components. METHODS: , 2018, a multi-country, cross-sectional study was conducted, based on semi-structured interviews with national IPC focal points of countries that pledged to the WHO 'Clean Care is Safer Care' challenge. Results and differences between regions and national income levels were summarized using descriptive statistics. FINDINGS: Eighty-eight of 103 (85.4%) eligible countries participated; 22.7% were low-income, 19.3% lower-middle-income, 23.9% upper-middle-income, and 34.1% high-income economies. A national IPC programme existed in 62.5%, but only 26.1% had a dedicated budget. National guidelines were available in 67.0%, but only 36.4% and 21.6% of countries had an implementation strategy and evaluated compliance with guidelines, respectively. Undergraduate IPC curriculum and in-service and postgraduate IPC training were reported by 35.2%, 54.5%, and 42% of countries, respectively. Healthcare-associated infection surveillance was reported by 46.6% of countries, with significant differences ranging from 83.3% (high-income) to zero (low-income) (P < 0.001); monitoring and feedback of IPC indicators was reported by 65.9%. Only 12.5% of countries had all core components in place. CONCLUSION: Most countries have IPC programme and guidelines, but many less have invested adequate resources and translated them in implementation and monitoring, particularly in low-income countries. Leadership support at the national and global level is needed to achieve implementation of the core components in all countries.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".