Using simulation to augment root cause analysis for patient safety incidents at a tertiary care women's and children's hospital: A qualitative feasibility study
Bibliographic record
Abstract
Background Rates of preventable harm in healthcare remain high despite comprehensive strategies to reduce and address patient safety issues. Newer methods such as simulation could add enhanced contextual understanding and may be a valuable tool to further understand and recommend changes based on patient safety incidents. However, the feasibility of simulation implementation needs to be considered to ensure its success. The Theoretical Domains Framework (TDF) provides a structure to identify factors of behavior that could influence the adoption of simulation as a safety tool in a healthcare environment. Objective To examine staff and clinician perspectives on the feasibility of a simulation-based patient safety initiative at a tertiary care women's and children's hospital. Methods Sixteen individual, semi-structured interviews were conducted with health center staff and physicians about the potential of simulation as a patient safety tool. Qualitative data was analyzed using a deductive content analysis, using the 14 TDF categories as a framework. Results Barriers and enablers to a simulation-based patient safety initiative were identified. Main barriers included social influences (cultural belief that simulation for patient safety is punitive), emotions (fear of judgment), and environmental context (staffing and time constraints). Main enablers included social influences (culture that values patient safety), goals/intentions (wanting to deliver safest care to patients), and beliefs about consequences (simulation as a valuable learning experience leading to improved care). Conclusions Though several barriers were identified, participants had recommendations for how to mitigate them and overall indicated support for using simulation as a patient safety tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".