Quantitative Assessment of Work-related Hand-arm Vibration Exposure Among Workers in the Construction, Underground Coal Mining, Wood Working, and Metal Working Industry: The German Hand-arm Vibration Study
Bibliographic record
Abstract
Standardized exposure assessments were conducted to quantify the historical occupational exposure to hand-arm vibration of workers in the German construction, underground coal mining, woodworking, and metalworking industries. A two-step approach was used to assess historical vibration exposure. In the first step, individual work histories were reconstructed by standardized personal interviews. The interview focused on the identification of relevant power tools used throughout the working life. In a second step, an equipment-exposure-matrix was constructed by industrial hygiene measurements. By linking the power tools in the work history to the equipment-exposure-matrix, individual daily and long-term vibration exposures can be quantified. A total of 423 power tools were identified for 5,115 exposure segments over a period of 50 years. 97.2% of the vibration values were based on industrial hygiene measurements. The total vibration value (a hv ) of the power tools used varied between 0.8 m/s 2 and 65.2 m/s 2 with a median value of 14.2 m/s 2 . The median value of cumulative vibration exposure is D hv = 121,971 (range: 23-3,374,640) m 2 /s 4 ∙day, corresponding to a daily vibration exposure of a hv(8) = 7 m/s 2 for 2489 working days (11.3 years). This study provides a detailed description of hand-arm vibration exposure among workers in the related industries studied. Our analyses indicate that the quantification of daily vibration exposure is often uncertain and should be interpreted with caution. In contrast, cumulative vibration exposure is a more reliable exposure parameter for describing general working conditions and for guiding the prevention and compensation of vibration-related health problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".