Exploring the Knowledge and Use of Standardised Nursing Terminology Across Australia
Bibliographic record
Abstract
Standardised nursing terminologies (SNTs) support the visibility of nursing work and documentation, enabling data sharing and comparison. An online survey assessed the knowledge and use of SNTs and revealed barriers and enablers to their use by Australian nurses. Just over half of the respondents were familiar with SNTs before the survey, a quarter reported a reasonable understanding of SNTs, just under half reported previous use of a SNT, and less than 14% indicated a current use of a SNT in their workplace. Perceived benefits to SNTs identified by respondents included a reduction in variation and the ability to evaluate the effectiveness of nursing care by measuring outcomes. Both barriers and enablers to the use of SNTs included education and training, standardisation and contextualisation across Australia, and integration into any electronic medical record system. Nurses are poorly informed on what SNTs are and how they can be leveraged to support their work and documentation. There is a need for an Australia-wide strategic approach to ensure the future of nurses' work is visible, and SNTs are purposefully and correctly implemented across the country.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".