A SEIPS-Based Analysis to Understand Safety Culture During Postpartum Hemorrhage
Bibliographic record
Abstract
Background/Objectives: Maternal mortality occurs at alarming rates in the United States. In 2018, there were 17 maternal deaths for every 100,000 births—double that of other high-income countries, including France and Canada. Postpartum hemorrhage (i.e., excessive blood loss during delivery or within the 24 h following) is a leading cause of maternal mortality and is a treatable condition if identified and managed in a timely manner. One aspect of work that impacts patient care during postpartum hemorrhage is the safety culture. The safety culture is the beliefs, values, and norms shared by members of the organization that influence their actions and behaviors. In this study, we use the Systems Engineering Initiative for Patient Safety (SEIPS) model to understand and describe how the sociotechnical system shapes safety culture during postpartum hemorrhage. Methods: We conducted interviews and focus groups with 29 clinicians to describe the work system and the barriers and facilitators during postpartum hemorrhage. Then, we inductively categorized the barriers and facilitators into emergent properties of sociotechnical systems related to safety culture. Results: We identified 45 barriers and 158 facilitators into five emergent properties related to the safety culture (i.e., staffing, communication, organizational management and leadership, organizational processes, and teamwork). The participants identified more positive aspects than negative, suggesting that the safety culture positively influences their actions and behaviors. Conclusions: Our results indicate that safety culture could be improved by redesigning the work system to mitigate barriers related to staffing, communication, organizational management, and teamwork that hinder the safety culture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".