A grounded theory of clinical nurses’ process of coping during COVID‐19
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To explore clinical nurses' process of coping during COVID-19 and develop a grounded theory that can be used by leaders to support clinical nurses during a disaster. BACKGROUND: The COVID-19 pandemic has provoked widespread disruption to clinical nurses' work. It is important to understand clinical nurses' processes of coping during disasters to support the nursing workforce during events such as global pandemics. DESIGN: We employed the Corbin and Strauss variant of grounded theory methodology, informed by symbolic interactionism, and applied the EQUATOR guidelines for qualitative research publication (COREQ). METHODS: Data collection entailed semi-structured interviews with experienced clinical nurses (n =20) across diverse settings. We analysed data by identifying key points in the nurses' coping processes inductively building concepts around these points. RESULTS: The predictor of nurses' outcomes in this grounded theory was their confidence in their ability to cope during the pandemic. When nurses lacked confidence, they experienced working in the context of acute COVID-a state of chaos and anxiety, with negative consequences for nurses. However, when nurses were confident in their abilities to cope with the pandemic, they experienced working in the context of chronic COVID, a calmer state of acceptance. There were many workplace factors that influenced nurses' confidence, including adequacy of personal protective equipment, clear information and guidance, supportive leadership, teamwork and adequate staffing. CONCLUSIONS: Understanding clinical nurses' experience of coping during COVID-19 is essential to maintain the nursing workforce during similar disasters. RELEVANCE TO CLINICAL PRACTICE: Nurse leaders can target areas that support nurses' confidence, such as adequate PPE and staffing. In turn, increased confidence enables clinical nurses to cope during disasters such as a global pandemic.
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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 | 0.029 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".