Perceptions of the effectiveness of Advanced Practice Nurses on a neurosurgery unit in a Canadian Tertiary Care Centre: A pre-and-post implementation design
Bibliographic record
Abstract
OBJECTIVES: A framework for the advanced practice nurse (APN) role was developed in our Canadian Tertiary Care Centre, delineating five domains of advanced nursing practice: clinical practice, consultation, research, education and leadership. The goal of this study was to evaluate perceptions of the effectiveness of the implementation of an innovative APN role on an in-patient Neurosurgery unit. METHODS: A pre-and-post implementation design, incorporating both qualitative and quantitative data, was utilized. An innovative APN role was implemented within the Neurosurgery program focusing on the clinical domain and required the successful candidates to be NP prepared. This APN role was designed to improve patient flow, documentation, communication and patient and staff satisfaction. Three primary outcomes were measured: pre-implementation questionnaire (nurses), post-implementation questionnaire (nurses and residents) and number of pages to the on-call resident. RESULTS: Survey scores by nurses and residents indicated improvement across all aspects studied. Average scores increased from 1.1 to 2.6, reflecting an overall statistically significant increase. The number of pages to the on-call resident also showed a decrease. CONCLUSION: Perceptions of patient care delivery and professional collaboration improved following implementation of the APN role. Responses indicated that APNs significantly impacted patient care and improved nurses and residents' job satisfaction.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".