Comparing FRAM and STAMP for occupational health and safety (OHS) and operational risks analysis: the case of data gloves in assembly 4.0 production
Bibliographic record
Abstract
known risk factor for motor vehicle crashes (MVCs).However, because it is a self-reported questionnaire, there are several limitations of ESS and the usefulness of ESS for predicting MVC is still disputable.Thus, we conducted a cross-sectional study to examine the association between ESS and MVC among Japanese taxi drivers.Material and Methods: Participants of our study consisted of 1,376 Japanese male taxi drivers.DS was assessed by ESS, and total ESS scores were categorized into quartile groups.MVC episode during the past five years was assessed by a self-administrated questionnaire.The multivariable generalized linear model was used to examine the association between ESS and MVC after adjusting for age, sex, body mass index, having snoring, sleep hours, length of service and driving distance per year.Results: The median (inter quartile range) ESS score was 4 (2:e6 ) and the proportion of MVC was 59.9 %.The prevalence ratio (95% confidence interval) of MVC for the highest quartile of ESS score was 1.18 (1.05e1.32),compared to the lowest quartile of ESS score (p for trend <0.01).Conclusions: We found a significant association between ESS and MVC among Japanese male taxi drivers after adjustment for potential confounders, despite relatively low mean ESS score of this study population.Further longitudinal studies are needed to elucidate the usefulness of ESS as a predictor of sleep-related MVCs among commercial drivers. 007
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".