A qualitative descriptive study of the impact of the COVID-19 pandemic on staff in a Canadian intensive care unit
Bibliographic record
Abstract
PURPOSE: We sought to explore the lived experiences of a professionally diverse sample of healthcare workers (HCWs) in a single intensive care unit (ICU) serving a large and generalizable Canadian population. We aimed to understand how working during the COVID-19 pandemic affected their professional and personal lives, including their perceptions of institutional support, to inform interventions to ameliorate impacts of the COVID-19 and future pandemics. METHODS: In this qualitative descriptive study, 23 ICU HCWs, identified using convenience purposive sampling, took part in individual semistructured interviews between July and November 2020, shortly after the first wave of the pandemic in Ontario. We used inductive thematic analysis to identify major themes. RESULTS: We identified five major themes related to the COVID-19 pandemic: 1) communication and informational needs (e.g., challenges communicating policy changes); 2) adjusting to restricted visitation (e.g., spending less time interacting with patients); 3) staffing and workplace supports (e.g., importance of positive team dynamics); 4) permeability of professional and personal lives (e.g., balancing shift work and childcare); and 5) a dynamic COVID-19 landscape (e.g., coping with constant change). The COVID-19 pandemic contributed to HCWs in the ICU experiencing varied negative repercussions on their work environment, including staffing and institutional support, which carried into their personal lives. CONCLUSION: Healthcare workers in the ICU perceived that the COVID-19 pandemic had negative repercussions on their work environment, including staffing and institutional support, as well as their professional and personal lives. Understanding both the negative and positive experiences of all ICU HCWs working during the COVID-19 pandemic is critical to future pandemic preparedness. Their perspectives will help to inform the development of mental health and wellbeing interventions to support staff during the COVID-19 pandemic and beyond.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".