Using Observation and Self-report to Predict Mean, 90th Percentile, and Cumulative Low Back Muscle Activity in Heavy Industry Workers
Bibliographic record
Abstract
Occupational injury research depends on the ability to accurately assess workplace exposures for large numbers of workers. This study used mixed modeling to identify observed and self-reported predictors of mean, 90th percentile, and cumulative low back muscle activity to help researchers efficiently assess physical exposures in epidemiological studies. Full-shift low back electromyography (EMG) was measured for 133 worker-days in heavy industry. Additionally, full-shift, 1-min interval work-sampling observations and post-shift interviews assessed exposure to work tasks, trunk postures, and manual materials handling. Data were also collected on demographic and job variables. Regression models using observed variables predicted 31-47% of the variability in the EMG activity measures, while self-reported variables predicted 21-36%. Observation-based models performed better than self-report-based models and may provide an alternative to direct measurement of back injury risk factors.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".