Supporting nurses’ recovery during and following the COVID-19 pandemic
Bibliographic record
Abstract
Research suggests that working during traumatic events can lead to deteriorating physical and mental health for nurses, a phenomenon that has been demonstrated during the coronavirus 2019 (COVID-19) pandemic. However, research has also shown that there are evidence-based strategies that can be used to assist nurses in their recovery from such events. Promoting awareness among individual nurses about the effects of COVID-19 enables them to adopt positive coping strategies, both on an individual and organisational level. This article details strategies including formal and informal debriefing, taking regular breaks, and using stress mitigation strategies during shifts. The article also discusses the potential for post-traumatic psychological growth. This acknowledges that while working in a healthcare environment during COVID-19 can be extremely challenging, it also enables nurses to experience personal growth such as the development of emotional intelligence. As nurses adapt to the 'new normal' of working during COVID-19, healthcare organisations should ensure that they provide nurses with the support that enables them to recover effectively.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".