Education Into Policy: Embedding Health Informatics to Prepare Future Nurses—New Zealand Case Study
Bibliographic record
Abstract
BACKGROUND: Preparing emerging health professionals for practicing in an ever-changing health care environment along with continually evolving technology is an international concern. This is particularly pertinent for nursing because nurses make up the largest part of the health workforce. OBJECTIVE: This study aimed to explore how health informatics can be included in undergraduate health professional education. METHODS: A case study approach was used to consider health informatics within undergraduate nursing education in New Zealand. This has led to the development of nursing informatics guidelines for nurses entering practice. RESULTS: The process used to develop nursing informatics guidelines for entry to practice in New Zealand is described. The final guidelines are based on the literature and are refined using an advisory group and an iterative process. CONCLUSIONS: Although this study describes the development of nursing informatics guidelines for nurses entering practice, the challenge is to move these guidelines from educational rhetoric to policy. It is only by ensuring that health informatics is embedded in the undergraduate education of all health professionals can we be assured that future health professionals are prepared to work effectively, efficiently, and safely with information and communication technologies as part of their practice.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".