Nursing Workforce Utilization: An Examination of Facilitators and Barriers on Scope of Practice
Bibliographic record
Abstract
Several reports have highlighted the need to address underutilization of health human resources, but barriers to and facilitators of role optimization for nurses are poorly understood. The purpose in this study was to understand the perceptions of nurses - Licensed Practical Nurses (LPNs), Registered Nurses (RNs) and Registered Psychiatric Nurses (RPNs) - of the extent to which they can work to full scope of practice and identify barriers and facilitators in optimizing their roles. As part of a mixed-methods study, semi-structured interviews were conducted with 167 acute care nurses (RNs, LPNs, RPNs and nurse managers) in three western Canadian health regions. Approximately 48% of all nurses interviewed felt they were working to full scope, at least some of the time. Barriers to working to full scope included heavy workload, high patient acuity, lack of time, poor communication and ineffective teamwork. Identified facilitators were working as a team, management and leadership support and support for continuing education. Barriers need to be addressed in light of nursing shortages, as these are closely related to job satisfaction and directly affect the retention and recruitment of all groups of nurses. Policies and strategies based on these findings must be developed to ensure that nurses can work to their full scope of practice.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".