Human Factors in the Wild
Bibliographic record
Abstract
This session will focus on lessons learned when developing solutions for real world problems in healthcare. Human factors (HF) practitioners bring unique knowledge, methods, and perspectives to work with clinicians to address numerous defects in the worksystem but require problem-solving skills that focus on solutions. The HF contribution may be in the area of process improvement (e.g., prevention of patient falls), systems design (e.g., designing crashcart), or identification of design flaws so robust intervention may be deployed (e.g., glucose meters). Six human factors practitioners who work within healthcare systems will present real world challenges which were discovered by HF methods, or which have heavy HF implications, but where direct HF influence on design lead to the ultimate effective solution. The session will be designed to lead to a discussion about how HF methods were adapted to individual problems at hand and to focus on developing solutions.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.189 | 0.041 |
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".