Adaptation and validation of a Korean-language version of the revised hospital survey on patient safety culture (K-HSOPSC 2.0)
Bibliographic record
Abstract
BACKGROUND: To date, there has been no universal and validated tool for measuring safety culture in Korea. The Hospital Survey on Patient Safety Culture (HSOPSC), version 2.0 was released by the Agency for Healthcare Research and Quality in 2019, but it had not yet been translated and assessed for use in Korea. The aim of this study was to assess the content validity and other psychometric properties of the Korean-language version of the HSOPSC 2.0. METHODS: Instrument adaptation was performed using a committee-based translation, cognitive interviews, and expert panel reviews. Confirmatory factor analysis was conducted on data obtained through an online survey from 526 registered nurses who worked on medical-surgical units in three teaching hospitals in South Korea. RESULTS: One item was dropped during the translation and adaption phase of the study as being a poor fit for the Korean healthcare context, resulting in excellent content validity. Confirmatory factor analysis supported the factorial structure of the K-HSOPSC 2.0. Correlations with an overall measure of patient safety provided further evidence of construct validity. Additionally, in comparing the results of this current study to those from U.S. research using the HSOPSC 2.0, it was found that Korean nurses assigned less positive scores to all dimensions of patient safety culture. CONCLUSION: Our findings provide evidence of the content validity, reliability, and construct validity of the K-HOSPSC 2.0 for measuring patient safety culture in South Korean hospitals. Hospital administrators can use this tool to assess safety culture and identify areas for improvement to enhance patient safety and quality of care.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".