The current state of Nursing Informatics – An international cross-sectional survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it