Factors shaping cleaning and disinfection practices during the COVID-19 pandemic: A qualitative evidence synthesis
Bibliographic record
Abstract
Background: Cleaning and disinfection of the physical environment is important as it can reduce the transmission of microorganisms. However, adherence to cleaning and disinfection protocols varies due to factors such as personal factors and external influences like resource availability, workload, and institutional support. Aim: To synthesise factors influencing the uptake of cleaning and disinfection interventions in healthcare and community setting in the context of COVID-19. Setting: These findings as seen in any country irrespective of setting. Method: Medline and World Health Organization (WHO) COVID-19 Research databases were searched from January 2020 to September 2022. The search identified 1618 studies, and analysis was performed using the thematic synthesis approach. The confidence in each review finding was ascertained using the Grading of Recommendations, Assessment, Development, and Evaluations-Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach. Results: Six analytical themes were identified. Cleaning and disinfection were seen as a cornerstone of patient care. Individual judgement, historic standards, norms and practices, ability to implement rapid practice guideline change and resource considerations were seen to influence the uptake of cleaning. Conclusion: There is a need for further qualitative studies in these areas, especially looking at the different interventions from an equity lens. Resource needs and availability were key factors influencing the uptake of cleaning and disinfection in both communities and health facilities. Contribution: This review shows important considerations for implementing infection prevention and control (IPC) interventions in the context of COVID-19.
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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 | 0.023 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".