Human Factors in the Wild
Bibliographic record
Abstract
The Institute of Medicine (2005) identified Human Factors (HF) engineering as an approach to promote better healthcare system design. The use of HF in healthcare has evidently delivered improvement in safety, quality, and productivity. Although existing literature shows the application of HF in practice, there is limited discussion of the integration of HF into healthcare operations, and the science of HF and its mechanism to deliver improved outcomes. This gap makes it difficult for healthcare professionals and management to see how HF can benefit their organization. Even if the potential benefits of integrating HF into healthcare organizations are understood, there is a lack of guidance on how to best deploy full-time HF practitioners. Despite the vast number of hospitals and healthcare systems around the world, only a few have actively and successfully engaged HF practitioners as part of their internal operations. This panel invites four healthcare HF practitioners, with diverse backgrounds and sub-specialties (Micro-, Physical-, and Macro-Ergonomics) to share their roles and contributions to their organization, and discuss their pathway to becoming integrated into their healthcare organization. This panel will provide guidance on how healthcare organizations can deploy and achieve the full benefit of their full-time HF practitioner (e.g., which unit/functional department to position HF and proper expectations of HF). Additionally, the panel will discuss insights for educators and budding HF practitioners on what it takes to advance their career in this challenging, yet literally life-saving industry.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.012 |
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".