‘The office of disaster management’ nurse managers' experiences during COVID‐19: A qualitative interview study using thematic analysis
Bibliographic record
Abstract
AIM: The purpose of this study was to understand the experiences of nurse managers during the COVID-19 pandemic. BACKGROUND: There is a growing body of knowledge about the experiences of clinical nurses during COVID-19. However, there is less evidence about the experiences of nurse managers during the pandemic. METHODS: Eight nurse managers, from acute care and outpatient settings, completed semistructured interviews about how their roles had changed during the pandemic, how they felt about these changes, and what had gone well or been difficult. Each participant was interviewed once, for 20-60 min. We used thematic analysis methods to analyse the interview transcripts. FINDINGS: Nurse managers had to coordinate care in a context of uncertainty and guidance that changed frequently. Participants found that their roles and responsibilities either expanded to include more duties, or they were asked to take on a completely new role, with no orientation or training. Nurse managers were expected to provide support to their staff and patients, but did not necessarily receive support themselves. Participants were expected to plan simultaneously for care during the pandemic and for a return to normal working conditions. These factors contributed to challenging and difficult participant experiences of managing during COVID-19. CONCLUSION: Nurse managers' experiences during COVID-19 are influenced by changes to their roles and the support they received. Nurse managers continue to support high-quality care despite working a difficult context. IMPLICATIONS FOR NURSING MANAGEMENT: Where possible, nurse managers can be supported to extend their roles or receive additional education and support if they are required to take on new responsibilities. Nurse managers require support in order to be a resource for their staff.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".