Organizational Factors Influencing Nurse Practitioners' Role Implementation in Acute Care Settings
Bibliographic record
Abstract
The purpose of this study was to explore the influence of organizational factors on the Acute Care Nurse Practitioner (ACNP) role implementation. A descriptive correlational design, incorporating quantitative and qualitative methods for data collection was used. The sample of convenience consisted of 57 ACNPs assigned to various medical and surgical programs within acute care hospitals. Ten ACNPs participated in the unstructured qualitative interviews. In addition to the interviews, data pertinent to various organizational factors, including role formalization, receptivity of the role by others, perceived autonomy, role strain, and additional factors that may interfere with role implementation, were collected through a self-report structured questionnaire. A four-diary day was completed to gather data on role implementation. Descriptive and correlational statistics were used to analyze the quantitative data. The qualitative data were content analyzed. The ACNPs engaged most frequently in activities reflective of the clinical practice component of the role and less frequently in the non-clinical components (i.e., education, administration, and research). Results of the quantitative and qualitative analyses indicated that lack of formal clear job description, conflicting demands and expectations, lack of receptivity of the role by others, lack of autonomy, and increased workload were negatively correlated with the ACNP role implementation. The ACNP role implementation varies across practice settings. This variability should be accounted for when examining outcomes of ACNP care.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".