An exploratory study of nurses’ ideas on how to improve compliance with the use of personal protective equipment when caring for patients on additional precautions
Bibliographic record
Abstract
Background: Healthcare-associated infections (HAI) present a significant risk to patients globally and they are listed as one of the most frequent adverse events in healthcare. The use of personal protective equipment (PPE) is one method of reducing transmission, yet despite the benefits of appropriate PPE being well documented, compliance by healthcare workers is poor. The aim of this study was to assess nurses’ ideas to improve compliance with PPE when caring for patients on additional precautions. Methods: The study took place at a 148-bed acute care hospital in British Columbia, Canada. A total of eight nurses, both licensed practical nurses (LPNs), and registered nurses (RNs) were selected from across the different wards based on their ability to provide the information required for the research study. Data was collected using qualitative semi-structured interviews to the point of data saturation, and consensus was obtained following the Delphi technique. Results: Eight themes emerged from the coding: risk assessment, knowledge/education, time/staffing, visible leadership, COVID-19 pandemic, patients, ward culture and PPE audits. The results were separated into two groups, influences on compliance and ideas for improving compliance. Conclusion: All eight themes contributed to the perception of risk which was identified as having the greatest influence on PPE compliance. These findings highlight the need for further research into the multifactorial approach to improving PPE compliance drawing from healthcare workers perspective.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".