One‐year follow‐up of hospital nurses' work experiences during the <scp>COVID</scp> ‐19 pandemic: A qualitative study
Bibliographic record
Abstract
AIM: To follow up on the experiences of Registered Nurses (RNs) working after 1 year of the COVID-19 pandemic in Canadian and American hospitals. DESIGN: Semi-structured interviews were conducted, and transcripts were analysed through a reflexive thematic analysis (RTA). METHODS: RNs (n = 19) first interviewed in the spring of 2020 were re-interviewed 1 year after their original interviews (May 25, 2021-June 25, 2021). Participants consisted of nurses residing in Canada and working in Ontario (n = 12) or American hospitals (n = 7), i.e., both local and cross-border nurses. RESULTS: Five themes were identified: (1) "You call us heroes, but you forgot us": Nurses described experiences of disrespect and stigma from their communities, their government, and their workplaces. (2) "A whole new level of busy": Nurses reported stressors both at home and at work that had increased exponentially throughout the pandemic. (3) "Running on empty": Nurses described burnout and mental health struggles including depression, irritation, and suicidal ideation; they coped using both adaptive and maladaptive strategies. (4) "The job of nursing is painful": Ongoing pandemic issues led nurses to re-evaluate their commitments to their units, their hospitals and the profession itself. (5) "Surviving an un-survivable day": Nurses shared positive moments at work and home that helped give them the strength to carry on. CONCLUSION: Significant investments will be required from hospital organizations and governments to ensure that healthcare systems continue to function safely for patients, their families and nurses. IMPACT: The purpose of this study was to understand and describe nurses' experiences after 1 year of working during the COVID-19 pandemic. Nurses reported feeling disrespected, overwhelmed, and burned out; many were looking to leave the profession. These findings will be of interest to nurses working on the frontline of the pandemic as well as hospital managers and policy makers. NO PATIENT OR PUBLIC CONTRIBUTION: In this investigation, nurses were the participants.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".