Untold Stories of the ER : Providing Care During the COVID-19 Pandemic as Narrated by Emergency Room Nurses in Toronto, Ontario, Canada
Bibliographic record
Abstract
As the COVID-19 pandemic has taken hold of Toronto, Ontario, Canada, and the world, it has highlighted many challenges healthcare workers face. Those nurses working in the emergency room (ER), settings that are under normal circumstances unpredictable and acute, have been particularly affected. This research aimed to explore the stories ER nurses tell to describe their experiences of working during the COVID-19 pandemic in Toronto, Canada. Narrative methodology was used to understand the thoughts, feelings, and problems facing ER nurses. The research study includes the stories of three Toronto-based ER nurses who share their experiences of working during the COVID-19 pandemic. Participants were interviewed twice, and data was analysed using the three-dimensional narrative inquiry space of time, sociality, and place. Plotlines of 'before they were heroes', 'hero', 'fall from grace', 'villain' and 'to be continued', organized each story. Resounding narrative threads emerged across the three narrative accounts and are presented as understandings. Threads that resonated across the stories include mistrust in leadership, fear and isolation, expectations and duty to care, nursing shortages, personal safety and PPE, workload and stress, moral and psychological distress, and lost voice. The findings of this inquiry offer a new context for understanding the thoughts, feelings, and problems facing ER nurses working in Toronto during the COVID-19 pandemic in a way that preserves, values, and respects the voices and stories of the nurses themselves, thus allowing for emotional healing while offering insight for nursing education, practice, and research.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".