“Goodbye … Through a Glass Door”: Emotional Experiences of Working in COVID-19 Acute Care Hospital Environments
Bibliographic record
Abstract
BACKGROUND: The severity of the COVID-19 health crisis has placed acute care nurses in dire work environments in which they have had to deal with uncertainty, loss, and death on a constant basis. It is necessary to gain a better understanding of nurses' experiences to develop interventions supportive of their emotional well-being. PURPOSE: The purpose of this study is to explore how nurses are emotionally affected working in COVID-19 acute care hospital environments. The research question is: What is the emotional experience of nurses working in COVID-19 acute care hospital environments? METHODS: We employed a narrative methodology that focused on participants' stories. Twenty registered nurses, who worked in six hospitals in the Greater Toronto Area in Canada, participated in interviews. A narrative analysis was conducted with a focus on content and form of stories. RESULTS: We identified three themes about working in COVID-19 acute care hospital environments: the emotional experience, the agency of emotions, and how emotions shape nursing and practice. CONCLUSION: In moving forth with pandemic preparations, healthcare leaders and governments need to make sure that a nurse's sacrifice is not all-encompassing. Supporting nurses' emotional well-being and resilience is necessary to counterbalance the loss and trauma nurses go through.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".