Exploring Resilience in Care Home Nurses: An Online Survey
Bibliographic record
Abstract
Resilience is considered a core capability for nurses in managing workplace challenges and adversity. The COVID-19 pandemic has brought care homes into the public consciousness; yet, little is known about the resilience of care home nurses and the attributes required to positively adapt in a job where pressure lies with individuals to affect whole systems. To address this gap, an online survey was undertaken to explore the levels of resilience and potential influencing factors in a sample of care home nurses in Northern Ireland between January and April 2022. The survey included the Connor-Davidson Resilience Scale, demographic questions and items relating to nursing practice and care home characteristics. Mean differences and key predictors of higher resilience were explored through statistical analysis. A moderate level of resilience was reported among the participants (n = 56). The key predictors of increased resilience were older age and higher levels of education. The pandemic has exposed systemic weakness but also the strengths and untapped potential of the care home sector. By linking the individual, family, community and organisation, care home nurses may have developed unique attributes, which could be explored and nurtured. With tailored support, which capitalises on assets, they can influence a much needed culture change, which ensures the contribution of this sector to society is recognised and valued.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".