A critical incident study of ICU nurses during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Intensive care unit nurses are providing care to COVID-19 patients in a stressful environment. Understanding intensive care unit nurses' sources of distress is important when planning interventions to support them. PURPOSE: To describe Canadian intensive care unit nurse experiences providing care to COVID-19 patients during the second wave of the pandemic. DESIGN: Qualitative descriptive component within a larger mixed-methods study. PARTICIPANTS AND RESEARCH CONTEXT: Participants were invited to write down their experiences of a critical incident, which distressed them when providing nursing care. Thematic analysis was used to analyze the data. ETHICAL CONSIDERATIONS: The study was approved by the ethics committee at the researchers' university in eastern Canada. RESULTS: A total of 111 critical incidents were written by 108 nurses. Four themes were found: (1) managing the pandemic, (2) witness to families' grief, (3) our safety, and (4) futility of care. Many nurses' stories also focused on the organizational preparedness of their institutions and concerns over their own safety. DISCUSSION: Nurses experienced moral distress in relation to family and patient issues. Situations related to insufficient institutional support, patient, and family traumas, as well as safety issues have left nurses deeply distressed. CONCLUSION: Identifying situations that distress intensive care unit nurses can lead to targeted interventions mitigating their negative consequences by providing a safe work environment and improving nurses' well-being.
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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 | 0.071 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".