Applying Human Factors Methods to Improve Workflow Safety in Transitioning to a New Special Care Nursery Unit
Bibliographic record
Abstract
The physical healthcare environment plays a pivotal role in shaping work processes, including workflow, equipment usage, human resources management, patient experience, and the ability of healthcare workers to provide safe care. Transitioning to a new space provides an opportunity to proactively identify and enhance safety measures for critical tasks and workflows. This paper presents a collaborative project between the Healthcare Insurance Reciprocal of Canada (HIROC) and Guelph General Hospital (GGH), aimed at improving workflow and workspace in GGH’s Special Care Nursery Unit (SCN) during its transition to a new space. The project was executed in two phases. Phase 1 involved contextual inquiry and observations over three days in the existing unit, which informed simulation scenarios of critical workflows and tasks. Phase 2 comprised simulation walkthroughs with 14 nurses in the new unit, conducted individually or in groups of up to four, to gather feedback. The data collected were used to identify opportunities to build resilience in the SCN, including considerations for improving communication and situation awareness. Survey results also indicated that participants found the simulations helpful for identifying areas to improve safety in the SCN.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".