Confirmatory Factor Analysis Affecting Aptitude in the Specialized Nursing Competency of Nursing Students
Bibliographic record
Abstract
The objective of the research is to explore the consistency of specialized nursing competency models of nursing students. The participants in this research are 165 Year 4 nursing students at Boromarajonani College of Nursing, Chakriraj, Thailand. The data was collected using a questionnaire consisting of 45 questions, answered with the use of a 5-level scale. The questionnaire was evaluated by three experts, and the questionnaire’s accuracy ratings ranged from .70 to 1.00. The internal reliability of the questionnaire was measured using Cronbach’s alpha coefficient, resulting in values of .763 for educational excellence, .757 for socio-economic factors, .806 for lifestyle, and .814 for personal factors. The study’s findings showed that competency. 1) Education throughout nursing student training. 2) The socio-economic context. 3) Habits pertinent to everyday existence. 4) Attitude towards the nursing profession. 5) GPAx represents the grade point average. 6) OSCE refers to the objective structured clinical examination. The confirmatory factor analysis of specialized nursing aptitude is consistent with the empirical data, Chi-Square = 14.226; df = 8; Relative Chi-Square = 1.778; p-value = .076; GFI = .974; NFI = .943; TLI = .951; CFI = .974; RMSEA = .069; RMR = .113. Each element has a standard element weight value of between .010 and .817, with a standardized weight value ranging from .010 to .817.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".