The current state of Nursing Informatics – An international cross-sectional survey
Bibliographic record
Abstract
An international survey to explore current and future trends in Nursing Informatics (NI) was done in 2015. This article explores responses to questions about: what should be done to further develop NI as an independent discipline; existing policies and standards influencing NI; perceived support towards NI as a discipline; and advice from NI specialists to students and emerging professionals. Nurse and allied health professionals in academia and practice were reached with snowball sampling. Open-ended questions were analysed with thematic content analysis and the mean and standard deviation is reported for the perceived support towards NI (scale ranging from 1 (not at all supportive) to 10 (very supportive)). A total of 507 respondents from 46 countries responded to the survey. Respondents reported mediocre support towards NI from the environment (M 5.79, SD 2.60). Results showed that NI education needs development to better meet practice demands, that current NI resources seem insufficient, that NI expertise is not used to its full potential in health institutions and the community, and that NI needs to show its value through research and increase visibility to be recognised among stakeholders worldwide. In conclusion, there is a need to clarify NI as a discipline and a need for strong leadership to impact policy making. An increase in NI teaching at undergraduate level in nursing as well as an increase in postgraduate NI programmes worldwide would better support practice demands. National policies and international white papers in NI are needed to guide resource distribution to better support 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".