Predicting Whole-Body Vibration Exposure in Canadian Prairie Farmers
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».