Revised competency inventory for evaluating nursing students
Bibliographic record
Abstract
Background and objective: To ensure high quality nursing education, a valid scale is needed to evaluate the core competences of nursing students. Insufficiencies of the Competency Inventory of Nursing Students currently used to measure competence in nursing students. The aim of this study was to revise Competency Inventory of Nursing Students and to validate its use for measuring competency in Junior college nursing students in terms of score distribution and dimensionality.Methods: The scale was refined in a series of three phases performed during 2015-2016 in Taiwan: (1) established the item set via literature reviews and content validity testing; (2) refined the item set based on self-reported data from 120 nursing students and confirmed the factor structure by confirmatory factor analysis in 244 nursing students; (3) established the validity and reliability of the final scale.Results: Analysis indicated that a 28-item scale with a 3-factor structure obtained the best fit to the data (χ2 = 752.56, p < .001, RMSEA = .069, SRMR = .043, CFI = .950, TLI = .946) and had an acceptable Cronbach α value (range .935 to .982). The strength of the inter-correlations among three latent variables was highly consistent with the conceptualization as a multifactorial construct.Conclusions and implications: The revised scale has satisfactory validity and reliability for measuring core competency in nursing students. Implications for practice: For employers concerned about the competency of recent graduates of associate degree nursing programs, the effective and comprehensive scale can be used for self-evaluation of competency in nurses and can also provide feedback for improving teaching and learning efficiency during the education of nurses.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".