Bibliographic record
Abstract
Informatics competency adoption is a recognized issue across nursing roles in digital health practice settings. Further, it has been suggested that the health system's inability to reap the promised benefits of electronic health/patient records is, in part, a manifestation of inadequate development of informatics competency by chief nurse executives (CNEs) and other clinicians (Amendola 2008; Simpson 2013). This paper will focus on CNE informatics competency and nursing knowledge development as it pertains to the Big Data revolution. With the paper's aim of showing how CNEs armed with informatics competency can harness the full potential of Big Data offering new opportunities for nursing knowledge development in their clinical transformation roles as eHealth project sponsors. It is proposed that informatics-savvy CNEs are the new transformational leaders of the digital age who will have the advantage to successfully advocate for nurses in leading 21st-century health systems. Also, transformational CNEs armed with informatics competency will position nurses and the nursing profession to achieve its future vision, where nurses are perceived by patients and professionals alike as knowledge workers, providing the leadership essential for safe, quality care and demonstrating nursing's unique contributions to fiscal health through clinically relevant, evidence-based practices (McBride 2005b).
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 | 0.038 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".