How Does the Workplace Environment Affect the Health and Decision of Registered Nurses to Remain in Critical Care?
Bibliographic record
Abstract
Background. Retaining registered nurses (RNs) in critical care environments (CCEs) face many challenges. Firstly, these settings have exceptional demands of staff because of higher environmental stress, higher patient acuity, and higher patient mortality rates relative to other nursing units. Secondly, the combination of stressors in CCEs can have significant effects on providers’ health, which can lead to high voluntary turnover rates. This can aggravate an already difficult situation, which requires, at substantial human and financial cost, the preparation of new, and often less experienced RNs to care for some of the most vulnerable patients. Aim. The study aims to understand the relationship of critical care RNs’ perceived CCEs, workloads, their health, and their intention to stay in their current employment setting. The dearth of research available concerning these relationships leaves the search for solutions without sufficient empirical data to inform strategies that would retain these highly-trained providers. Research Methods. A cross-sectional study assessed the interaction of RNs’ work environment, their health, and their intent to stay in the CCE. Data was obtained from a sample of 302 critical care RNs across Alberta, Canada, which allowed for negative binomial and logistic regression modelling analyses. RNs were also asked what interventions would optimize their work environment and retain their critical care services. Results. Critical care RNs who scored their CCEs higher had lower sick time incidence and decreased intention to leave. Other important factors for RNs’ decision to stay in the CCE included their workload, increased educational opportunities, and increased availability of part-time scheduling. Conclusions. This study results showed strong positive relationships between CCEs, RNs’ health, and RNs’ turnover intention. RNs specifically request workload optimization, increased flexibility with shift rotations, and increased education opportunities on their units to optimize the environment and retain their services. Given the high demands associated with such services, decision-makers should consider these findings when anticipating the needs of RNs and patients. This would, at the very least, assure RNs that hospitals care as much for their health as the patients that RNs serve.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".