Bibliographic record
Abstract
Limited informatics competency uptake is a recognized nursing leadership challenge impacting digital practice settings. The health system's inability to reap the promised benefits of EHRs is a manifestation of inadequate development of informatics competencies by chief nurse executives (CNEs) and other clinicians. Through the application of Transformational Leadership Theory (TL), this discussion paper explains how informatics competencies enable CNEs to become transformational nursing leaders in digital health allowing them to meet their accountabilities to lead integrated, high-quality care delivery through evidence based practices (EBPs). It is proposed that successful CNE eHealth sponsors will be those armed with informatics competencies who can drive health organizations' investment in technology and innovation. Finally, some considerations are suggested in how nurse informaticists globally play a critical role in preparing our existing and future CNEs to fulfill their transformational leader roles in the digital age.
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".