Cost-efficient assessment of biomechanical exposure in occupational groups, exemplified by posture observation and inclinometry
Bibliographic record
Abstract
OBJECTIVES: This study compared the cost efficiency of observation and inclinometer assessment of trunk and upper-arm inclination in a population of flight baggage handlers, as an illustration of a general procedure for addressing the trade-off between resource consumption and statistical performance in occupational epidemiology. METHODS: Trunk and upper-arm inclination with respect to the line of gravity were assessed for three days on each of 27 airport baggage handlers using simultaneous inclinometer and video recordings. Labor and equipment costs associated with data collection and processing were tracked throughout. Statistical performance was computed from the variance components within and between workers and bias (with inclinometer assumed to produce "correct" inclination angles). The behavior of the trade-off between cost and efficiency with changed sample size, as well as with changed logistics for data collection and processing, was investigated using simulations. RESULTS: At similar total costs, time spent at trunk and arm inclination angles >60 ° as well as 90 (th)percentile arm inclination were estimated at higher precision using inclinometers, while median inclination and 90th percentile trunk inclination was determined more precisely using observation. This hierarchy remained when the study was reproduced in another population, while inclinometry was more cost-efficient than observation for all three posture variables in a scenario where data were already collected and only needed to be processed. CONCLUSIONS: When statistical performance was measured only in terms of precision, inclinometers were more cost-efficient than observation for two out of three posture metrics investigated. Since observations were biased, inclinometers consistently outperformed observation when both bias and precision were included in statistical performance. This general model for assessing cost efficiency may be used for designing exposure assessment strategies with considerations not only of statistical but also cost criteria. The empirical data provide a specific basis for planning assessments of working postures in occupational groups.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".