Lived Experiences of Hemodialysis Health Care Workers during the COVID-19 Pandemic: A Qualitative Study from the Quebec Renal Network
Bibliographic record
Abstract
Key Points Hemodialysis workers' well-being and work were affected by the COVID-19 pandemics. Effective communication strategies and taking into account psychological distress are ways to mitigate the challenges faced by health care workers. Background The COVID-19 pandemic has disrupted health systems and created numerous challenges in hospitals worldwide for patients and health care workers (HCWs). Hemodialysis centers are at risk of COVID-19 outbreaks given the difficulty of maintaining social distancing and the fact that hemodialysis patients are at higher risk of being infected with COVID-19. During the COVID-19 pandemic, HCWs have had to face many challenges and stressors. Our study was designed to gain HCWs' perspectives on their experiences of the impacts of the COVID-19 pandemic in hemodialysis units. Methods Semistructured interviews were conducted with 22 HCWs (nurses, nephrologists, pharmacists, social workers, patient attendants, and security agents) working in five hemodialysis centers in Montreal, between November 2020 and May 2021. The content of the interviews was analyzed using thematic analysis. Results Four themes were identified during the interviews. The first was the impact of COVID-19 on work organization, regarding which participants reported an increased workload, a need for a consistent information strategy, and positive innovations such as telemedicine. The second theme was challenges associated with communicating and caring for dialysis patients during the pandemic. The third theme was psychological distress experienced by hemodialysis staff and the psychosocial impact of COVID-19 on their personal lives. The fourth theme was recommendations made by participants for future public health emergencies, such as maintaining public health measures, ensuring an adequate supply of protective equipment, and developing a consistent communication strategy. Conclusions During the first and second waves of the COVID-19 pandemic, HCWs working in hemodialysis units faced multiple challenges that affected their well-being and their work. To minimize challenges for HCWs in hemodialysis during a future pandemic, the health care system should provide an adequate supply of protective equipment, develop effective communication strategies, and take into account the psychological distress related to HCWs' professional and personal lives.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".