Impact of COVID-19 in Quebec Hemodialysis Units: Health Care Providers' Experiences
Bibliographic record
Abstract
Background: Chronic kidney disease is a risk factor for the severe form of COVID-19 and the hemodialysis unit represents a high-risk setting for virus transmission. Healthcare providers (HCPs) have the duty to keep patients safe and healthy, and also to protect themselves from the virus. The objective of this study was to gather health care providers' experiences working in dialysis units. Methods: We conducted semi-directed interviews by phone or video with 21 HCPs working in 6 hemodialysis units - nurses, nephrologists, pharmacists, social workers, security agent and housekeeping attendant - between November 2020 and May 2021. The content of the interviews was analyzed using thematic content analysis. Results: Participants identified positive and negative impact of Covid-19 pandemic. In their professional life, HCPs declared developing more collaboration, creativity and mutual support. However, due to the pandemic restrictive measures and lack of resources, HCPs felt a lot of distress not being able to provide adequate care for patients' needs. Participants also reported disruption in communication between HCPs and patients because of physical distancing and wearing a mask. They also described problems associated with patient transportation leading to delays or even absence of patients to their treatment. In their personal life, some HCPs declared being concerned by these new challenges at work and reported difficulties balancing work and family life. Also, most of them feared to contaminate their family and adopted certain routine cleaning to alleviate this fear. Conclusions: HCPs working in hemodialysis unit faced multiple challenges during the Covid-19 pandemic that impacted their wellbeing. However, they have shown high level of resilience and dedication to ensure health care delivery and to support hemodialysis patients. Funding: Government Support - Non-U.S.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".