Factors influencing fatigue in UK nurses working in respiratory clinical areas during the second wave of the Covid-19 pandemic: An online survey
Bibliographic record
Abstract
Aims and objectives: This study explores UK nurses' experiences of working in a respiratory clinical area during the COVID-19 pandemic over winter 2020. Background: During the first wave of the pandemic, nurses working in respiratory clinical areas experienced significant levels of anxiety and depression. As the pandemic has progressed, levels of fatigue in nurses have not been assessed. Methods: A cross-sectional e-survey was distributed via professional respiratory societies and social media. The survey included Generalised Anxiety Disorder Assessment (GAD7), Patient Health Questionnaire (PHQ9, depression), a resilience scale (RS-14) and Chalder mental and physical fatigue tools. The STROBE checklist was followed as guidance to write the manuscript. Results: Despite reporting anxiety and depression, few nurses reported having time off work with stress, most were maintaining training and felt prepared for COVID challenges in their current role. Nurses reported concerns over safety and patient feedback was both positive and negative. A quarter of respondents reported wanting to leave nursing. Nurses experiencing greater physical fatigue reported higher levels of anxiety and depression. Conclusions: Nurses working in respiratory clinical areas were closely involved in caring for COVID-19 patients. Nurses continued to experience similar levels of anxiety and depression to those found in the first wave and reported symptoms of fatigue (physical and mental). A significant proportion of respondents reported considering leaving nursing. Retention of nurses is vital to ensure the safe functioning of already overstretched health services. Nurses would benefit from regular mental health check-ups to ensure they are fit to practice and receive the support they need to work effectively. Relevance to clinical practice: A high proportion of nurses working in respiratory clinical areas have been identified as experiencing fatigue in addition to continued levels of anxiety, depression over winter 2020. Interventions need to be implemented to help provide mental health support and improve workplace conditions to minimise PTSD and burnout.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| 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, 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".