Factors affecting Korean neonatal nurses’ pain care: Psychometric evaluation of three instruments
Bibliographic record
Abstract
AIM: The purpose of this study was to evaluate the psychometric properties of the Korean-language versions of Pain Knowledge and Use (PKU-K), Collaboration and Satisfaction About Care Decisions (CSACD-K), and Environmental Complexity Scale (ECS-K). METHODS: A cross-sectional design was used with a convenience sample of 159 Korean nurses in seven neonatal intensive care units (NICUs). The data were collected by surveying the nurses with the PKU-K, CSACD-K, and ECS-K. Internal consistency reliability was assessed and Horn's parallel analysis, a confirmatory factor analysis, and a convergent construct validity test were conducted in order to evaluate the psychometric properties of the instruments. RESULTS: The PKU-K, CSACD-K, and ECS-K exhibited strong internal consistency reliability. Horn's parallel analysis showed four factor structures for the PKU-K, one for the CSACD-K, and three for the ECS-K. The confirmatory factor analysis showed a good model fit for the PKU-K and CSACD-K, but the ECS-K model showed a poor fit. Most factor loadings were statistically significant. The CSACD-K's convergent validity was supported by significant correlations for collegial nurse-physician relations with a validated instrument. CONCLUSION: The findings support the reliability and validity of the PKU-K, CSACD-K, and ECS-K for measuring nurses' knowledge about neonatal pain care, nurse-physician collaboration, and the work environment in NICUs. However, the ECS-K needs further refinement before it is applied to Korean NICU 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 | 0.008 | 0.023 |
| 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 | 0.002 |
| 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".