Researching safety culture: deliberative dialogue with a restorative lens
Bibliographic record
Abstract
Safety culture is a key component of patient safety. Many patient safety strategies in health care have been adapted from high-reliability organizations (HRO) such as aviation. However, to date, attempts to transform the cultures of health care settings through HRO approaches have had mixed results. We propose a methodological approach for safety culture research, which integrates the theory and practice of restoration science with the principles and methods of deliberative dialogue to support active engagement in critical reflection and collective debate. Our aim is to describe how these two innovative approaches in health services research can be used together to provide a comprehensive effective method to study and implement change in safety culture. Restorative research in health care integrates socio-ecological theory of complex adaptive systems concepts with collaborative, place-sensitive study of local practice contexts. Deliberative dialogue brings together all stakeholders to collectively develop solutions on an issue to facilitate change. Together these approaches can be used to actively engage people in the study of safety culture to gain a better understanding of its elements. More importantly, we argue that the synergistic use of these approaches offers enhanced potential to move health care professionals towards actionable strategies to improve patient safety within today's complex health care systems.
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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 | 0.127 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.019 | 0.116 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".