Construct validity of advanced practice nurse core competence scale: an exploratory factor analysis
Bibliographic record
Abstract
BACKGROUND: Determining the core competence of advanced practice nurses is foundational for promoting optimal design and implementation of advanced practice nursing roles. Core competencies specific to the contexts of the advanced practice nurse in Hong Kong have been developed, but not yet validated. Thus, this study aims to assess the construct validity of advanced practice nurse core competence scale in Hong Kong. METHODS: We performed a cross-sectional study using an online self-report survey. Exploratory factor analysis was used to examine the factor structure of a 54-item advanced practice nurse core competence scale through principal axis factoring with direct oblique oblimin rotation. A parallel analysis was conducted to determine the number of factors to be extracted. The Cronbach's α was computed to evaluate the internal consistency of the confirmed scale. The STROBE checklist was used as reporting guideline. RESULTS: A total of 192 advanced practice nurse responses were obtained. Exploratory factor analysis led to the final 51-item scale with a three-factor structure, which accounted for 69.27% of the total variance. The factor loadings of all items ranged from 0.412 to 0.917. The Cronbach's alpha of the total scale and three factors ranged from 0.945 to 0.980, indicating robust internal consistency. CONCLUSION: This study identified a three-factor structure of the advanced practice nurse core competency scale: client-related competencies, advanced leadership competencies, and professional development and system-related competencies. Future studies are recommended to validate the core competence content and construct in different contexts. Moreover, the validated scale could provide a cornerstone framework for advanced practice nursing roles development, education, and practice, and inform future competency research nationally and internationally.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".