Optimizing enactment of nursing roles: redesigning care processes and structures
Bibliographic record
Abstract
Background: Effective and efficient use of nursing human resources is critical. The Nursing Role Effectiveness Model conceptualizes nursing practice in terms of key clinical role accountabilities and has the potential to inform redesign efforts. The aims of this study were to develop, implement, and evaluate a job redesign intended to optimize the enactment of registered nurse (RN) clinical role accountabilities. Methods: A job redesign was developed and implemented in a single medical patient care unit, the redesign unit. A mixed-methods design was used to evaluate the job redesign; a second medical patient care unit served as a control unit. Data from administrative databases, observations, interviews, and demographic surveys were collected pre-redesign (November 2005) and post-redesign (October 2007). Results: Several existing unit structures and processes (eg, model of care delivery) influenced RNs' ability to optimally enact their role accountabilities. Redesign efforts were hampered by contextual issues, including organizational alignment, leadership, and timing. Overall, optimized enactment of RN role accountabilities and improvements to patient outcomes did not occur, yet this was predictable, given that the redesign was not successful. Although the results were disappointing, much was learned about job redesign. Conclusion: Potential exists to improve the utilization of nursing providers by situating nurses' work in a clinical role accountability framework and attending to a clear organizational vision and well-articulated strategic plan that is championed by leaders at all levels of the organization. Health care leaders require a clear understanding of nurses' role accountabilities, support in managing change, and leadership development opportunities. Keywords: nursing scope of practice, nursing role enactment, nursing role accountabilities, job redesign, leadership
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".