Predicting Whole-Body Vibration Exposure in Canadian Prairie Farmers
Bibliographic record
Abstract
Direct workplace whole-body vibration exposure assessment provides ecological validity for evaluating health risk in epidemiological studies, yet it is complex and expensive in practical applications. Exposure prediction modeling could be a cost-efficient alternative to directly assessing occupational vibration exposures. The objective of this study was to model directly measured whole-body vibration exposures with predictors from machinery, farm, and self-reported characteristics among Canadian prairies farmers. As per ISO 2631-1, whole-body vibration data were measured on the seat surface at three axes (x, y, z), then summarized into vector sums of the root-mean-squared (RMS) acceleration and the vibration dose value (VDV). All candidate predictors were obtained via questionnaires and onsite observations. A total of 87 whole-body vibration measurements were collected from 40 male farm workers located at 21 central Saskatchewan farms. Using log-transformed RMS and time-standardized VDV outcomes, modeling started from the bivariate analysis where predictors with P-values < 0.2 were considered eligible for multivariate analysis. With random effects of 'farm' and 'farmer', a series of mixed-effects models were constructed through the manual backward elimination method. Final models were internally validated by 1000 bootstrapped samples. The RMS model explained 47.7% of the variance in the directly measured RMS vector sum, with 42.7% obtained from five predictors of 'horsepower', 'transmission', 'vehicle year', 'jerk/jolt frequency', and 'seat bottom-out frequency', while the VDV model explained 19.5% of the variance in the directly measured VDV vector sum, with 11.6% described by the same five predictors as the RMS model. Predictive ability of the RMS model among 1000 bootstrapped samples can be anticipated to range from 14.3 to 69.1%, which may be considered adequate as exposure assessment tool for uses of epidemiological studies. The percentage of variance explained ranged from 0 to 40.5% for the VDV model, which is not robust and therefore likely not appropriate for use in survey-based exposure prediction. Whole-body vibration exposure modeling remains valuable, but is challenging in farming; the described model variance may increase with a more comprehensive list of candidate variables collected and quantified at machinery, farm, and farmer level. Predictors identified in the current and future models may provide a better understanding of how whole-body vibration exposure is modified, guide farmer's future decision on updating equipment, and allow for the development and initiation of interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".