Evaluating Safety Culture Changes over Time with the Emergency Medical Services Safety Attitudes Questionnaire
Bibliographic record
Abstract
Introduction The correlation between patient outcomes and the safety culture in healthcare organisations draws special attention to tools that can measure safety culture in such organisations. One of the advantages of such tools is their ability to identify changes in safety climate, which can support healthcare organisations in detecting and understanding trends, which might have otherwise been overlooked. Objective To evaluate the ability of a standard survey to capture long-term safety climate changes in pre-hospital care. Methods The previously validated Emergency Medical Services Safety Attitudes Questionnaire was administered in one regional base hospital program, which delegates to six pre-hospital emergency care services. The survey was administered over two consecutive years, thus allowing us to measure safety climate changes over time. Results Significant differences were found between the first and second years of the survey in specific services. Conclusions While we cannot identify the specific causes for the change in scores in the various services between the two survey years, we can draw some inferences. We suggest that the small changes that tend to reflect a consistent change across all services are the result of training and educational initiatives, while greater changes in some of the services reflect a change in the attitude of the paramedics to the service, driven by changes in operational procedures within the service. Our findings demonstrate that the questionnaire can capture safety climate changes over time in pre-hospital emergency 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".