A National Education Strategy To Develop Nursing Informatics Competencies
Bibliographic record
Abstract
Advances in the sophistication of information and communication technologies offer nursing practitioners opportunities for better information management, more complete documentation of their work, and knowledge development to support evidence-based nursing practice. However, a nursing culture that recognizes and adopts the contributions of technology to practice is required to take advantage of these opportunities. The nature of this change suggests a shift in emphasis from specialists in Nursing Informatics (NI) to NI being integrated into all four domains of nursing practice. The magnitude of change required on individual, organizational and professional levels points to the need for Nursing Informatics education strategies on a national level. Recognizing the role and history of NI specialists, defining NI and the required NI competencies are necessary first steps in developing such a plan. Expanding and adapting the educational infrastructure required to support this initiative follows. A working committee at the national level with representatives from a number of stakeholder groups is currently working on a National Nursing Informatics Project to address these issues. This article summarizes key points of an initial discussion paper.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".