Psychometric Evaluation of the Korean Version of Hospital Survey on Patient Safety Culture
Bibliographic record
Abstract
OBJECTIVES: This study evaluated the psychometric properties of the Korean-language version of the Hospital Survey on Patient Safety Culture (HSOPSC) among Korean nurses. METHODS: We analyzed secondary data from 801 direct care nurses working at a tertiary, private, nonprofit, teaching hospital in South Korea. Descriptive statistics, internal consistency coefficients, and intercorrelations were calculated. The latent factor structure of the HSOPSC was examined using exploratory structural equation modeling techniques, which account for the noncontinuous nature of ordinal data. RESULTS: Although a majority of subscales showed acceptable to good internal consistency, 4 dimensions (staffing, overall perceptions of patient safety, organizational learning-continuous improvement, and nonpunitive response to errors) had reliability levels less than 0.6. The HSOPSC items loaded somewhat diffusely on 3 subscales: staffing, teamwork across units, and organizational learning-continuous improvement. Correlations between the 12 HSOPSC factors indicated discriminant validity. Convergent validity was supported by correlations between the 12 subscales and a single-item outcome variable, namely, patient safety grade. Several items did not load well on their respective subscales, but most items fit the underlying theoretical model implied by the HSOPSC, resulting in an acceptable model fit (confirmatory fit index = 0.985, root mean square error of approximation = 0.034, weighted root mean square residual = 0.54). CONCLUSIONS: Despite the acceptable model fit of the Korean version of HSOPSC, the psychometric properties of this instrument require further investigation to ensure it is an effective tool to measure patient safety culture and identify areas for improvement in the Korean health care system.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".