Intensive care unit organisation and nurse outcomes: A cross-sectional study of traditional and “hot-floor” structures
Bibliographic record
Abstract
Aim: To explore the relationship between the practice environment and nurse outcomes in two Intensive Care Unit (ICU) models.Background: Internationally the demand for intensive care is increasing. A large capacity multi-specialty integrated critical care service, the “hot-floor”, is emerging as the preferred organisational model. Benefits include resource consolidation and improved utilisation, operational synergies, operational flexibility and demand management. A large clinical workforce with commensurate frontline management, education and support positions are required. The association between these factors, within the ICU hot-floor work environment, and nurse outcomes is not known.Methods: Registered nurses (RNs) working in two ICUs, one a hot-floor model and one traditional ICU, completed a structured questionnaire. Nurse perceptions of work-life and organisational factors, and dimensions of burnout were examined using the Practice Environment Scale-Nursing Work Index (PES-NWI) and Maslach’s Burnout Inventory (MBI).Results: Units matched on service level characteristics, training accreditation, patient casemix, operational and clinical care processes. Nurses in had similar demographic characteristics, professional attributes and experience. Workforce structures were also similar though the hot-floor had relatively less dedicated resources for frontline nurse management and clinical education positions. Hot-floor nurses worked more paid overtime and were redeployed less frequently to external wards. Nurse manager leadership and support was less effective, and nurses expressed lower personal accomplishment.Conclusions: Improved demand management achieved through greater operational flexibility is a key driver for the hot-floor model. Planning for enhanced organisational effectiveness requires corresponding improvements in the work environment to optimise nurse retention to ensure organisational sustainability.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".