Cleaning and disinfecting protocols for hospital environmental surfaces: A systematic review of the literature
Bibliographic record
Abstract
Background: Healthcare associated infections are a leading cause of illness and death in the United States and across the world. Environmental surfaces are considered non-critical, although recent evidence suggests that the built environment may contribute to the transmission of pathogens. Ineffective cleaning and disinfecting of environmental non-critical surfaces may increase risk of transmitting nosocomial pathogens leading to hospital acquired infections among hospital patients.Objective: This systematic review identifies elements of cleaning and disinfecting protocols, synthesizing the evidence to evaluate cleaning protocols that effectively reduce surface contamination and minimize risk of hospital acquired illness.Methods: A systematic literature review was conducted with a clearly formulated research question and systematic approach to identify publications, select relevant studies, critically appraise the research through analysis of reported data, and reported the results according to the Cochrane methodology.Results: In total, 245 studies were initially identified with 19 studies meeting inclusion criteria. Emerging categories include chemical application methods, chemical application time, cleaning type and frequency, and interventions for training and monitoring.Conclusions: Establishing adequate cleaning protocols for hospital environments is a complex process which requires consideration of multiple components including mechanical action, chemical application materials, types of cleaning, chemical contact times, education and training of EVS staff, cleaning monitoring and feedback, no-contact cleaning methods, and self-disinfecting surfaces. Recommendations for protocol development based on the study results are provided.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".