The influence of job rotation and task order on muscle fatigue: A deltoid example
Bibliographic record
Abstract
Despite frequent use in industry, job rotation lacks robust confirmation as an effective method to limit exposure. This study investigated two tasks that involved the deltoid muscle. We examined two major factors in the context of muscle fatigue: the presence of rotation between tasks, and the order of task rotation if rotation was present. Participants performed four task combinations (coded AA, AB, BA, BB) of two tasks that were intended to produce fatigue (A: repetitive shoulder flexion; B: repetitive shoulder abduction). All tested conditions resulted in lower maximum force production capability (mean range of 78-88% of original strength), in this order of decreasing magnitude: BB --> AB --> BA --> AA, though differences between successive levels were not always significant. Specific muscle results supported this progression of strength decreases. For tasks with different muscular demands (AB and BA), it was less fatiguing to rotate between them than to only perform the more demanding task (BB). The order of rotation between tasks (AB vs. BA) did not influence muscle fatigue indicators. These findings help to assess the effectiveness of rotating between different tasks in reducing muscular fatigue or exposure. They also indicated a low apparent influence of task order on terminal fatigue characteristics for the task combinations evaluated.
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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 | 0.005 |
| 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".