Knowledge Gaps Regarding APN Roles: What Hospital Decision-Makers Tell Us
Bibliographic record
Abstract
The implementation of advanced practice nursing (APN) roles can yield improvements in patient and health system outcomes, and supportive leadership is integral in facilitating the implementation of such roles. The purpose of this study was to explore the awareness and understanding of APN roles among hospital decision-makers, and to learn about the information they require and the ways in which they prefer to receive that information. Fifteen administrators and leaders from two multi-site acute care organizations were interviewed. Their practical knowledge of APN roles was based on experience developing the roles or working with APNs in hospital programs. The most common sources of APN information were internal contacts (i.e., APNs) and documents from nursing organizations. Participants reported difficulty distinguishing between the roles of nurse practitioners (NPs) and clinical nurse specialists (CNSs), and identified knowledge regarding CNS roles as their greatest need. They required specific information regarding the "value-added" benefits offered by an APN role. Strategies to address the knowledge gaps of healthcare leaders are urgently needed in order to support the implementation of new APN roles and to sustain existing ones.
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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 | 0.025 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".