“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 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".