Human Body Mechanics of Pushing and Pulling: Analyzing the Factors of Task-related Strain on the Musculoskeletal System
Bibliographic record
Abstract
The purpose of this review is to name and describe the important factors of musculoskeletal strain originating from pushing and pulling tasks such as cart handling that are commonly found in industrial contexts. A literature database search was performed using the research platform Web of Science. For a study to be included in this review differences in measured or calculated strain had to be investigated with regard to: (1) cart weight/ load; (2) handle position and design; (3) exerted forces; (4) handling task (push and pull); or (5) task experience. Thirteen studies met the inclusion criteria and proved to be of adequate methodological quality by the standards of the Alberta Heritage Foundation for Medical Research. External load or cart weight proved to be the most influential factor of strain. The ideal handle positions ranged from hip to shoulder height and were dependent on the strain factor that was focused on as well as the handling task. Furthermore, task experience and subsequently handling technique were also key to reducing strain. Workplace settings that regularly involve pushing and pulling should be checked for potential improvements with regards to lower weight of the loaded handling device, handle design, and good practice guidelines to further reduce musculoskeletal disease prevalence.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".