Bibliographic record
Abstract
Nursing leaders play a critical role in creating and enacting a vision for collaborative practice with advanced practice nurses (APNs). In this special issue, Nancy Carter and colleagues have identified many important influences and outcomes of successful nursing leadership in the context of promoting advanced practice nursing roles. The authors make a strong case for the importance of nursing leadership to facilitate large-scale systems change, noting the multiple levels on which nursing leaders work to ensure advanced practice nursing roles are well introduced to improve patient care. Nursing leadership can move an innovation like advanced practice nursing practice forward toward the "tipping point," when the new idea takes hold and becomes socially acceptable and desired, when the early adopters have influenced the early majority and about 15 to 20% of the population have adopted the idea (Berwick 2003). In many ways our nursing leaders have achieved this with advanced practice nursing roles, and we should celebrate. APNs are now more common, and certainly members of the public are proud to speak of the roles APNs play in their health services. An idea that once captured the minds of a select few has spread, thanks in large part to the nursing leaders who had a vision, believed in an idea, fought for it and worked to embed the change in the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.052 | 0.060 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".