Development and Validation of a Patient Safety Culture Questionnaire in Acute Geriatric Units
Bibliographic record
Abstract
BACKGROUND: Older patients (≥65 years) are exposed to more harm resulting from adverse events in hospitals than younger patients. Theoretical considerations and empirical findings suggest that safety culture is the key to improving the quality of health care. OBJECTIVE: To describe the development of a German-language instrument for assessing patient safety culture (PSC) and its reliability and validity; to verify criterion validity by means of a cross-sectional analysis of the impact of PSC on clinical quality that compares acute geriatric units with a sample from intensive care, surgery and trauma surgery departments, and to report variations in the PSC profile between these groups. METHODOLOGY: Using a review of existing safety culture surveys, multidimensional scaling procedures and expert interviews, we tested the content and convergent validity of a 158-item questionnaire completed by 508 physicians and nurses from 31 acute geriatric units and 7 comparison departments. Criterion validity was verified by various regression models with a self-reported measure of adverse events. Differences in PSC profiles were analyzed using a one-factorial ANOVA and regression models. RESULTS: We identified 7 constructs of PSC and demonstrated substantial convergent and criterion validity. In the acute geriatric units, higher levels of 'management commitment to patient safety' and lower levels of 'error fatalism' were associated with a reduced incidence of medical errors. In the comparison group, only the variable 'active learning from mistakes' was relevant for safety performance. Our results also indicate that acute geriatric units display higher standards than the comparison group in all the aspects of patient safety examined. CONCLUSION: It is possible to measure salient features of PSC using a valid and reliable survey. Some aspects of PSC are more closely related to safety events than others. In acute geriatric units, patient safety appears to be influenced mainly by management's determination of how things are done whereas improvement of the system itself in a more incremental manner is required in the other high-risk ward types.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".