Posture, machinery and risk: ergonomic characterization of industrial driving work in forklifts and electric pallet trucks
Bibliographic record
Abstract
Objectives. The use of machinery in industrial tasks like load transportation exposes workers to ergonomic risk factors, particularly from non-neutral postures, increasing the chance of musculoskeletal disorders (MSDs). This study aimed to assess posture-related ergonomic risk among forklift and electric pallet truck operators. Methods. Conducted at a beverage bottling plant in Viña del Mar, Chile, the cross-sectional study involved 75 operators. Personal and occupational data were gathered through questionnaires, and the rapid entire body assessment (REBA) method was used to evaluate postural risk based on video analysis during machine operation. Results. Findings revealed that 49.3% of workers were at high ergonomic risk and 50.7% at very high risk, with pallet truck operation strongly associated with increased ergonomic risk (odds ratio [OR] 6.7; 95% confidence interval [2.5, 19.5]). The most significant risk factors were operating electric pallet trucks and being aged under 30 years. Pallet truck operation was strongly associated with increased ergonomic risk (crude OR 6.7), especially in workers with less experience. Conclusion. The study concludes that targeted ergonomic interventions, such as redesigning handle height or improving vibration isolation systems in electric pallet trucks, may be essential to protect younger and less experienced workers.
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 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 | 0.000 | 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".