Measuring low back injury risk factors in challenging work environments: An evaluation of cost and feasibility
Bibliographic record
Abstract
BACKGROUND: Measuring low back injury risk factors in field research presents challenges not encountered in laboratory environments. METHODS: We compared the practical application of five measurement methods (observations, interviews, electromyography (EMG), inclinometry, and vibration monitoring) for 223 worker days in 50 heavy-industry worksites in western Canada. Data collection successes, challenges, costs, and data detail were documented for each method. RESULTS: Measurement success rates varied from 42.2% (seatpan accelerometer) to 99.6% (post-shift interview) of worker days assessed. Missed days for direct monitoring equipment were primarily due to explosive environments, workplace conditions likely to damage the equipment, and malfunctions. Costs per successful measurement day were lowest for interviews (approximately 23 dollars), about 10-fold higher for observations and inclinometry, and more than 20-fold higher for EMG and vibration monitoring. CONCLUSIONS: Costs and successful field performance need to be weighed against the added data detail gained from monitoring equipment when making choices about exposure assessment techniques for epidemiological studies.
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".