Gender- and time-varying postural and discomfort responses during prolonged driving
Bibliographic record
Abstract
The objective of this study was to conduct gender- and time-based comparisons of postural and discomfort responses during prolonged simulated driving. Prolonged driving has been linked with low back disorders (LBD), however underlying mechanisms of pain or injury are not well understood despite many efforts to delineate the biomechanics of automobile seating. Twelve males and 12 females were exposed to one hour of simulated automobile driving. Body postures, body-seat pressure distributions, and ratings of perceived discomfort were documented and gender- and time-based comparisons were performed. Females exhibited approximately 10° greater changes in lumbo-pelvic orientation when upright standing postures were compared to those in automobile seating. Attributed to gender-based differences in body size was the finding that males experienced significantly greater body-seat interface pressures. Many of the postural, pressure, and discomfort measures varied significantly over 30 to 45 minutes, and these variations were mostly consistent between genders. Many variables examined varied as a function of gender and duration of driving. Future biomechanical investigations of automobile seating design for more effective LBD prevention should consider these potential effects in order to gain further insight into potential low back pain- or injury-generating mechanisms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".