INFECTION PREVENTION AND CONTROL COMPLIANCE AMONG HOSPITAL HEALTHCARE WORKERS: DETERMINANTS, INTERVENTIONS, AND PATIENT OUTCOMES, A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract Background: Infection prevention and control (IPC) depends on consistent healthcare worker (HCW) adherence to core practices. Suboptimal compliance contributes to healthcare-associated infections (HAIs) and avoidable harm. Methods: A PRISMA-aligned systematic review was conducted using PubMed Central to identify hospital-based original research evaluating IPC compliance, determinants of compliance, interventions to improve compliance, or patient outcomes. Eligible designs included randomized and nonrandomized interventional studies and observational studies among hospital HCWs. Ten original studies were included for Results, and nine additional PMC articles were used to frame the Introduction and Discussion. Results: Ten original studies (multi-country; ICUs and general wards) addressed hand hygiene and, or standard precautions. Interventional studies consistently improved hand hygiene compliance, with examples including increases from 50.17% to 71.75% alongside reduced HAI and CAUTI rates in a Saudi university hospital, 30.9% to 69.5% after an ICU educational program in Egypt, and 32.1% to 39.4% after a multimodal program in Tunisia. Determinants of compliance were repeatedly linked to training, resource availability, workload, time pressure, safety climate, and monitoring, feedback. Conclusion: IPC compliance among hospital HCWs is modifiable. Multimodal interventions improve compliance and can translate to better patient outcomes when coupled with surveillance and leadership accountability.
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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 | 0.020 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".