Psychometric evaluation of the Austrian version of the Nurse Professional Competence Scale Short Form (NPC–SF–AUT)
Bibliographic record
Abstract
Background and objective: The continuous assessment of nursing competence is internationally established and an important task in the further development of professional nursing. The Nurse Professional Competence Scale Short Form is a potentially appropriate instrument to assess the competence of Austrian Registered Nurses (RN). However, the translated and Austrian-specific culturally adapted version of the scale has not yet been sufficiently psychometrically tested. The aim of this study was to test the validity and internal consistency of the Austrian version of the Nurse Professional Competence Scale Short Form.Methods: We conducted an exploratory cross-sectional study. Between October 2021 and January 2022, Registered Nurses from a total of 16 hospitals were invited to assess their competencies using the Austrian version of the Nurse Professional Competence Scale Short Form. Principal axis factor analysis with Promax rotation was performed to test construct validity. Both Cronbach's Alpha and McDonald’s Omega coefficients were used to evaluate internal consistency.Results: Data from a total of 576 Registered Nurses were included in the psychometric evaluation. Both the Kaiser-Meyer-Olkin coefficient (KMO = 0.958) and the significant Bartlett test (χ2 = 12430.988; df = 595; p < .001) indicated appropriate fit of the data for factor analysis. Using principal axis factor analysis with Promax rotation, five factors were extracted, explaining a total of 60.5% of the variances. The Nurse Professional Competence Scale Short Form German Austrian language version (NPC–SF–AUT) thus comprises 35 items representing the five factors “Health promotion and safeguarding” (13 items), “Multi-professional cooperation and development” (7 items), “Process-guided nursing care” (5 items), “Inclusive decision-making” (5 items) and “Rule-governed professional practice” (5 items). Both the factor-specific Cronbach’s Alpha and McDonald’s Omega coefficients confirmed good to excellent (α = 0.83-0.92; Ω = 0.83-0.92) internal consistency of the NPC-SF-AUT.Conclusions: The NPC–SF–AUT is a valid and internal consistent instrument for the self-assessment of RNs’ competence in Austria. The instrument can be used for the continuous assessment of nursing competence and thus contribute to the advancement of the nursing profession.
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.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".