Spotlight on the leadership and management of intensive care units
Bibliographic record
Abstract
This issue of Nursing in Critical Care casts a light on the leadership and management of intensive care units (ICUs). To set the scene for this special issue, we are very honoured to include two thought-provoking guest editorials and a critical commentary from eminent critical researchers and practitioners. The editorials by Fiona Timmins and her colleagues and Gillian Colville respectively provide insightful comments on these papers' contribution from two different perspectives: the commitment of nurses and nursing to the current and future development of nursing leadership and management in ICUs1 and the importance of staff well-being in management considerations.2 The critical commentary by Sarah Sumner reflects on the impact of the COVID-19 pandemic on the work environment and mental health of ICU nurses.3 The research and review papers in this special collection tackle various facets of intensive care leadership and management, ranging from ‘classic’ topics such as turnover, intent to stay, staffing levels, workload, burnout, safety, job satisfaction and leadership styles to factors that are gaining attention in recent decades, such as moral distress, compassion fatigue, compassion satisfaction, moral sensitivity and courage, relatives' participation in care and emotional intelligence. As expected, the past and future impact of the COVID-19 pandemic features prominently in this issue. The issue also showcases the cross-sectional survey as a very popular research design in investigating these topics. Nonetheless, the issue also includes a retrospective cohort study, literature reviews and an instrument construction/validation study. As usual, this special issue offers an international perspective with contributions from the United Kingdom, Italy, Greece, Iran, China, Turkey, the United States, Egypt, Brazil, Spain and Poland. We trust that you will enjoy reading, learning from and being inspired by the papers in this special issue, as much as we did while compiling it.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".