Psychological experiences of nurses in COVID-19 isolation wards in China: A qualitative examination
Bibliographic record
Abstract
Objective: This study explores the psychological experiences and factors affecting the psychological health of isolation ward nurses during the COVID-19 pandemic in China. Findings from this study will inform on future initiatives that can support frontline nurses, improve their ability to cope with public health emergencies, and create effective nursing management strategies.Methods: A qualitative phenomenological research method was applied. In-depth interviews with 10 first-line clinical nurses in isolation wards during the COVID-19 pandemic were conducted. Collected data were analyzed with the Colaizzi seven-step analysis method.Results: Three themes were emerged, including the evolution of the psychological health of nurses, positive factors that affect psychological health, and negative factors that affect psychological health. Nurses experienced various changes to their psychological health during the pandemic. When nurses were supported by team members, appreciated by their patients, and cared for by their organizations, their psychological health was enhanced. When nurses felt disconnected from their families and experienced physical discomfort due to protective equipment, their psychological health was negatively impacted.Conclusions: Changes in the psychological health of isolation ward nurses should be constantly monitored. Proper strategies should be implemented to reduce the physical discomfort experienced by nurses due to protective equipment, increase perceptions of familial and workplace support, and determine ways to increase displays of patient appreciation for nursing work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".