Systems Safety: Identifying Facilitators, Barriers, and Failure Modes to Quality Patient Care on a Postnatal Unit
Bibliographic record
Abstract
BACKGROUND: The postpartum period is critical for safeguarding the health and well-being of birthing parents. After delivery, birthing parents will spend an average of 24 to 48 hours in the hospital during the postnatal stay where health care workers (HCWs) monitor them, identify treatment needs, assist with breastfeeding, conduct depression assessments, and provide education. Identifying clinical system factors hindering the provision of high-quality care is critical to improving care and addressing the challenges faced by HCWs and birthing parents during inpatient postpartum care. Thus, this study was to identify barriers and facilitators that impact HCWs' work and care delivery within the postnatal unit. METHODS: The study involved a secondary analysis of observational data collected in a postnatal unit of a large, academic hospital in the United States. Barriers and facilitators were identified and coded using the System Engineering and Initiative for Patient Safety 2.0 model and the Healthcare Performance Improvement Taxonomies of Individual and System Failure Modes. RESULTS: A total of 87 barriers and 18 facilitators were identified. Common barriers included challenges with communication between HCWs, insufficient and unclear patient education, space constraints, and insufficient tools and technology. Facilitators included informed consent with patients and accessible educational tools that support HCWs in the provision of care. CONCLUSIONS: The findings from this research can inform the design and improvement of postnatal units to improve patient safety and support HCWs in providing high-quality, responsive, and patient-centered 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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".