Leveraging Expertise to Improve Safety – Insights and Lessons Learned From Collaborative Applied Human Factors Safety Work in a Maternal Neonatal Care Setting
Bibliographic record
Abstract
Collaboration across Canada’s healthcare system is critical to improving safety in the high-risk area of maternal neonatal care. Oak Valley Health (OVH) and the Healthcare Insurance Reciprocal of Canada (HIROC) collaborated on two applied safety projects, leveraging expertise from both organizations. These projects sought to identify opportunities to improve teamwork, communication and workflow across the Childbirth and Children’s Service program at OVH. In Project 1, human factors specialists from HIROC implemented a series of front-line-focused ownership techniques to understand opportunities to improve teamwork and communication to support patient safety across the program. In Project 2, the study team used observations to map nurses’ movement during obstetrical triage and conducted a perceived mental workload assessment of nursing staff in the obstetrical triage area. We discuss the approach and results, as well as lessons learned from this collaborative work that can be shared in other healthcare contexts. Key learnings include insights into project scoping, and leveraging the clinical, human factors and other expertise from multiple organizations to proactively improve safety.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".