Nursing stress and satisfaction outcomes resulting from implementing a team nursing model of care in a rural setting
Bibliographic record
Abstract
Objective: With increasing demands to provide a cost efficient nursing service, changes to nursing skill mix are being implemented globally. Team nursing as a model of care is seen as a way to address both patient care and safety issues. The aim of this study was to explore job satisfaction (JS) and stress outcomes of nursing staff when introducing team nursing as model of care within the Australian healthcare environment.Methods: An experimental study was utilised. Nursing staff (n = 63) were surveyed, using the Person Centred Nursing Index (PCNI) tool, prior to the implementation of a team nursing model of care and then again six months post implementation of the model (n = 64). Data was analysed to determine if there was a statistically significant difference in the average theme between pre and post surveys.Results: Nursing stress (NS) was reduced and JS was increased post implementation of the new model of care. JS and organisational traits, JS and work stress (WS), were positively related and increased post implementation. WS and nursing care (NC), organisational traits and NC were positively related but showed no statistically significant change after the implementation. This study demonstrated that in introducing a new model of care, levels of stress staff increased yet unexpectedly JS also improved.Conclusions: Decisions to adopt team nursing as the model of care should be based on a broad range of considerations not simply on fiscal considerations and should include staff readiness, staff mix and supportive measures to introduce a changed model of care.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".