Canadian critical care nurses experiences on the front lines of the COVID-19 pandemic: a qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: Recent pandemics have provided important lessons to inform planning for public health emergencies. Despite these lessons, gaps in implementation during the COVID-19 pandemic are evident. Additionally, research to inform interventions to support the needs of front-line nurses during a prolonged pandemic are lacking. We aimed to gain an understanding of critical care nurses' perspectives of the ongoing pandemic, including their opinions of their organization and governments response to the pandemic, to inform interventions to improve the response to the current and future pandemics. METHODS: This sub-study is part of a cross-sectional online survey distributed to Canadian critical care nurses at two time points during the pandemic (March-May 2020; April-May 2021). We employed a qualitative descriptive design comprised of three open-ended questions to provide an opportunity for participants to share perspectives not specifically addressed in the main survey. Responses were analyzed using conventional content analysis. RESULTS: One hundred nine of the 168 (64.9%) participants in the second survey responded to the open-ended questions. While perspectives about effectiveness of both their organization's and the government's responses to the pandemic were mixed, most noted that inconsistent and unclear communication made it difficult to trust the information provided. Several participants who had worked during previous pandemics noted that their organization's COVID-19 response failed to incorporate lessons from these past experiences. Many respondents reported high levels of burnout and moral distress that negatively affected both their professional and personal lives. Despite these experiences, several respondents noted that support from co-workers had helped them to cope with the stress and challenges. CONCLUSION: One year into the pandemic, critical care nurses' lived experiences continue to reflect previously identified challenges and opportunities for improvement in pandemic preparedness and response. These findings suggest that lessons from the current and prior pandemics have been inadequately considered in the COVID-19 response. Incorporation of these perspectives into interventions to improve the health system response, and support the needs of critical care nurses is essential to fostering a resilient health workforce. Research to understand the experience of other front-line workers and to learn from more and less successful interventions, and leaders, is needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".