Perspectives on the use of personal protective equipment by acute care providers caring for patients on COVID-19 medical and critical care units
Bibliographic record
Abstract
Background: Studies have provided strong evidence that personal protective equipment (PPE) effectively reduces the risk of transmitting highly infectious emerging diseases among healthcare providers (HCPs). However, literature examining this phenomenon specifically within the context of the COVID-19 pandemic remains limited. This study explored the behaviours and contributing factors influencing HCPs’ use of PPE, as well as how they established a sense of safety while caring for patients with COVID-19. Methods: We conducted 22 semi-structured interviews with HCPs who provided direct care to patients with COVID-19 in the medical and critical care units of a large urban hospital in Vancouver, British Columbia. An interpretive description approach was used to understand staff narratives and identify key themes. Results: Staff reported high confidence in their PPE practices, citing factors such as emerging evidence and guidance, infection prevention and control (IPAC) protocols, occupational experience, specific PPE workflows, and point-of-care risk assessments. Within the broader context of the COVID-19 pandemic in British Columbia, staff identified several factors that influenced their PPE behaviours: PPE accessibility and availability, staff education, environmental reminders, staffing levels, environmental cleaning, physical space, time constraints, patient acuity and workload, PPE fatigue, the evolution of SARS-CoV-2 variants, vaccination status, occupational culture, and systemic trust. Conclusions: Overall, our findings highlight the importance of a relational approach in supporting HCPs to keep both patients and colleagues safe during the pandemic. By fostering trust and open communication, infection control practitioners (ICPs) can help HCPs navigate the challenges of misinformation and psychological stress. Identifying the factors that shape PPE behaviour enables ICPs to design targeted interventions that address frontline staff needs and promote effective PPE practices. Ultimately, the development of realistic, context-sensitive guidelines — along with addressing the mental and informational challenges faced by HCPs – is crucial to enhancing safety and adherence to infection control practices in future public health emergencies.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".