Bibliographic record
Abstract
In Brief Human factors is a term that is being heard with increasing frequency in the healthcare setting. What does it mean? How is it relevant to clinical engineering? And how can a clinical engineer identify and track human factors problems and report them to the Food and Drug Administration (FDA)? The following article addresses these and other questions and aims to provide clinical engineers with a better understanding of the science of human factors and how it can be used to improve patient safety. The information presented below is a summary of an educational Web cast entitled "Human Factors: Tools and Tips for Clinical Engineers and Medical Device Users" that was organized by FDA's Medsun program. (The Medical Device Safety Network [Medsun] is an important patient safety initiative that builds relationships with the clinical community to better understand device-related problems. The program consists of a network of 350 US healthcare facilities that use an Internet-based adverse event reporting system to notify FDA of existing and potential problems with medical devices.) A replay is available online at http://www.fda.gov/MedicalDevices/Safety/MedSunMedicalProductSafetyNetwork/ucm112724.htm. This article also summarizes information from FDA's human factors Web site, http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/HumanFactors/ucm124851.htm. Human factors is a term that is being heard with increasing frequency in the healthcare setting. What does it mean? How is it relevant to clinical engineering? And how can a clinical engineer identify and track human factors problems and report them to the Food and Drug Administration (FDA)? The following article addresses these and other questions and aimed to provide clinical engineers with a better understanding of the science of human factors and how it can be used to improve patient safety. The information presented below is a summary of an educational Web cast entitled "Human Factors: Tools and Tips for Clinical Engineers and Medical Device Users" that was organized by FDA's Medsun program. (The Medical Device Safety Network [Medsun] is an important patient safety initiative that builds relationships with the clinical community to better understand device-related problems. The program consists of a network of 350 US healthcare facilities that use an Internet-based adverse event reporting system to notify FDA of existing and potential problems with medical devices.) A replay is available online at http://www.fda.gov/MedicalDevices/Safety/MedSunMedicalProductSafetyNetwork/ucm112724.htm. This article also summarizes information from FDA's human factors Web site, http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/PostmarketRequirements/HumanFactors/ucm124851.htm.
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 | 0.026 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.065 | 0.070 |
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".