Designing Impactful Human Factors Research Programs in Healthcare
Bibliographic record
Abstract
Several human factors (HF) research studies conducted by HumanEra, our research team, generated interest from the healthcare community and appeared to have strong impact on clinical practice. We believe these studies demonstrated valuable characteristics that could support HF professionals devise research programs that translate findings into clinical practice more effectively, and thereby improve patient safety. Three characteristics were identified, including 1) sustained project funding from an organization with broad jurisdictional responsibilities so that the research has broader system applicability, 2) the presence of a multidisciplinary advisory group to validate findings and engage key stakeholders to later champion the study findings into the healthcare system and 3) the use of multiple methods that build toward implementation efforts. Our studies have resulted in guidance, and other tools, for stakeholders across the healthcare system, including national regulatory organizations, manufacturers, healthcare institutions, clinical educators, clinicians and patients. They have also spurred major areas of ongoing work and funding for our team. We believe that the tendency for our work to trigger additional studies of this type is because funders recognize that proactive and exploratory risk assessment has tremendous value in preventing or reducing patient harm and associated downstream costs. We hope other teams will be able to utilize our experiences to enhance their research efforts and build the profile of HF in healthcare to further support this work.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".