Evaluation of Austrian nurses’ competence and factors related to it: An exploratory cross-sectional study
Bibliographic record
Abstract
Background and objective: The continuous assessment of Registered Nurses' (RNs') competence is important at individual, organizational and systemic levels. Qualifications, the professional working environment as well as experience influence nursing competence. Nursing has significantly changed over the last 25 years in Austria, but RNs’ competence has not been evaluated so far. The aim of the study was to assess nursing competence of Austrian RNs, considering relevant influencing factors.Methods: An exploratory cross-sectional study was conducted. Between October 2021 and February 2022 a total of 841 RNs from 16 Austrian hospitals self-assessed their nursing competencies using the Austrian version of the Nurse Professional Competence Scale Short Form (NPC-SF-AUT). Multiple subgroup analyses with regard to theoretically reasonable influence factors on nursing competence were performed to explore differences in the extent of RNs’ competence.Results: Competencies in scale factors Multi-professional development and cooperation as well as in Health promotion and safeguarding were found as lowest. The overall work experience as well as further education and training had a significant influence on nursing competence, whereas the type of nursing education (vocational vs. higher education), the professional understanding of nursing care and the type of medical discipline did not.Conclusions: Appropriate structures must be implemented to ensure the development and application of Austrian RNs’ basically acquired competencies in 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".