Emerging Professionals’ Observations of Opportunities and Challenges in Nursing Informatics
Bibliographic record
Abstract
The importance of nursing informatics (NI) is highlighted because of changing healthcare landscapes in response to rising digital health and technology integration and use. However, NI education, competency requirements and roles are not standardized across the world, and the potential of NI is modestly understood internationally. This paper explores opportunities and challenges in NI discussed in a panel at the 14th International Congress on Nursing and Allied Health Informatics. The panel was organized by the International Medical Informatics Association's - Nursing Informatics Working Group's Student and Emerging Professionals group. Discussions during the panel session were synthesized and analyzed using content analysis. Results indicate that challenges in NI education, career opportunities and roles continue to exist across healthcare settings and regions. Findings suggest that the following issues need attention: (1) collaboration to build stronger infrastructure to guide NI education, research and practice; (2) improved visibility and appreciation of NI; and (3) greater dissemination of evidence of NI in various health settings. This paper offers recommendations for nurse leaders on strategies to address these issues in NI at the local, regional and global levels.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".