Developing and validating the Clinical Competence Questionnaire: A self-assessment instrument for upcoming baccalaureate nursing graduates
Bibliographic record
Abstract
Background: Although researchers have evaluated nurse competence in past studies, few have focused on the compe- tence levels of nursing students immediately prior to graduation. Additionally, many of the competence scales were not supported with strong evidence of reliability or validity. The purpose of this study was to develop and test the psycho- metric properties of the Clinical Competence Questionnaire (CCQ) that measures the perceived clinical competence of upcoming baccalaureate nursing graduates. Methods: The Clinical Competence Questionnaire was developed based on Patricia Benner’s “From Novice to Expert” model. This developed instrument was evaluated in a cross-sectional study. A total of 340 baccalaureate students in their final semester of a 2-year RN-to-BSN program in Taiwan completed and returned the questionnaire. Out of the 340 students, data from 293 students who did not have work experience were used to test reliability and validity of the scale. The instrument was tested for content, construct, and criterion-related validity. Results: The Cronbach’s alpha for the entire CCQ was .98. Content and known-groups validity were confirmed. Principal component analysis showed a high degree of explanation of competence and revealed four components of competence: nursing professional behaviors, core nursing skills, general performance, and advanced nursing skills. Conclusion: The results from our study indicate the CCQ demonstrates good reliability and validity for measuring the perceived clinical competence of upcoming baccalaureate nursing graduates. The CCQ is also a useful tool and is easy to administer for the self-assessment of nursing clinical competence. Study limitations and further recommendations for nursing are discussed.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".