Determinants and magnitudes of manual force strengths and joint moments during two-handed standing maximal horizontal pushing and pulling
Bibliographic record
Abstract
Pushing and pulling are common occupational exertions that are increasingly associated with musculoskeletal complaints. This study focuses on the sensitivity of shoulder capacity to gender, handle height, exertion type (push or pull) and handle orientation for these tasks. All factors except for handle orientation influenced unilateral and total manual force strength (p < 0.01), with exertion type being the most influential. Interaction effects also existed between handle height and exertion type. Additionally, joint moments at the shoulders and low back were influenced by all factors studied (p < 0.01), with exertion type again being most influential. Knowledge of the relative influence of multiple factors on shoulder capacity can provide guidance regarding these factors when designing or evaluating occupational pushing and pulling tasks for a diverse population. Practitioner Summary: pushing and pulling comprise nearly half of all manual materials handling tasks. Practitioners often assess, design or modify these tasks while incorporating constraints, including manual force direction and handle interface. This study provides guidance to aid design of pushing and pulling tasks in the context of shoulder physical capacity.
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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".