Advancing Measurement of Patient Safety Culture
Bibliographic record
Abstract
OBJECTIVE: To examine the psychometric and unit of analysis/strength of culture issues in patient safety culture (PSC) measurement. DATA SOURCE: Two cross-sectional surveys of health care staff in 10 Canadian health care organizations totaling 11,586 respondents. STUDY DESIGN: A cross-validation study of a measure of PSC using survey data gathered using the Modified Stanford PSC survey (MSI-2005 and MSI-2006); a within-group agreement analysis of MSI-2006 data. Extraction Methods. Exploratory factor analyses (EFA) of the MSI-05 survey data and confirmatory factor analysis (CFA) of the MSI-06 survey data; Rwg coefficients of homogeneity were calculated for 37 units and six organizations in the MSI-06 data set to examine within-group agreement. PRINCIPAL FINDINGS: The CFA did not yield acceptable levels of fit. EFA and reliability analysis of MSI-06 data suggest two reliable dimensions of PSC: Organization leadership for safety (alpha=0.88) and Unit leadership for safety (alpha=0.81). Within-group agreement analysis shows stronger within-unit agreement than within-organization agreement on assessed PSC dimensions. CONCLUSIONS: The field of PSC measurement has not been able to meet strict requirements for sound measurement using conventional approaches of CFA. Additional work is needed to identify and soundly measure key dimensions of PSC. The field would also benefit from further attention to strength of culture/unit of analysis issues.
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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 | 0.070 | 0.245 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".