Changing the model of care delivery: nurses’ perceptions of job satisfaction and care effectiveness
Bibliographic record
Abstract
AIM: To examine nurses' perceptions of job satisfaction, empowerment, and care effectiveness following a change from team to a modified total patient care (TPC) delivery model. BACKGROUND: Empirical data related to TPC is limited and inconclusive. Similarly, evidence demonstrating nurses' experience with change and restructuring is limited. METHOD: A mixed method, longitudinal, descriptive design was used. Registered nurses and licenced practical nurses in two acute-care nursing units completed quantitative and qualitative surveys. Lewin's change theory provided the framework for the study. RESULTS: No significant change in job satisfaction was observed; however, it was less than optimal at all three time-periods. Nurses were committed to their jobs but relatively dissatisfied with their input into the goals and processes of the organization. Client care was perceived to be more effective under TPC. CONCLUSION: Job satisfaction remained consistent following the transition to TPC. However, nurses perceived that client care within the modified TPC model was more effective than in the previous model. IMPLICATIONS FOR NURSING MANAGEMENT: Nursing administration must work collaboratively with nurses to improve processes in nursing practice that could enhance nurses' job satisfaction and improve client care delivery.
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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 | 0.006 | 0.016 |
| 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 | 0.000 | 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".