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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.378 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".