Central Trends in Nursing Informatics
Bibliographic record
Abstract
In July 2014, Taipei, Taiwan, hosted the biennial International Congress on Nursing Informatics (NI2014), titled East Meets West: eSMART+. This inaugural event for the Asia Pacific geographic region was organized by Taiwan’s Nursing Informatics Association and International Medical Informatics AssociationNursing Informatics Special Interest Group (IMIA-NISIG). The Congress attracted more than 500 participants from 28 countries, including about 80 students. There were more than 300 presentations, panel presentations, student papers, and poster sessions. At a specially organized student event, members of the Nursing Informatics Students’ Working Group had the opportunity tomeet and seek consensus about trends seen in the presentations. This meeting was followed by a collaborative writing effort, inspired by a similar publication by students in health geography.1Our goal in this article is to highlight the central themes presented at the Congress from the perspective of student participants and to provide a historical reference of the current topics, methodologies, and vision that inform and advance current nursing informatics research. We explore the five topics of interest that we found at NI2014: (1) standardized terminologies, (2) big data, (3) patient activation, (4) nursing informatics education and competencies, and (5) mobile health. To recognize some recent methodological trends in nursing informatics, we also present a methodological highlight regarding triangulation in health information technology.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.014 |
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".