Analysis of Relationships between Body Load and Training, Work Methods, and Work Rate: Overcoming the Novice Mason’s Risk Hump
Bibliographic record
Abstract
Masons regularly perform physically strenuous and demanding duties that may exceed a safe limit. Such activities can contribute to an early retirement for masons, resulting in a shortage of skilled craft workers. Previous ergonomic studies have observed that workers develop safer and more productive work techniques as they gain experience. This study aims to analyze relationships between body loads, experience, and work methods. Specifically, we expanded a previous pilot study by increasing the number of participants from 21 masons to 66 masons. Participants completed a prebuilt standard concrete masonry unit (CMU) lead wall using 45 CMUs. Motion capture suits were used to capture masons’ motions, and a combined biomechanical-productivity analysis was carried out to determine the loads experienced by major body joints. Exploiting the larger dataset, this study assessed how different experience groups load their joints and adjust their work techniques as the work height changes. The results suggested that experienced journeymen adopt similar work techniques distinct from those of less experienced workers. Further, training apprentices to adopt these work methods can help reduce occupational injuries and improve productivity. The results show that the journeymen with more than 20 years of experience adopt safer and more productive work techniques distinct from those of less experienced workers. The present study contributes to the body of knowledge on masons’ safety and productivity by providing an in-depth understanding of the linkage between body loads, work experience, techniques, and productivity. Additionally, the findings in this study are expected to have a greater impact when they are adopted to apprentice-training methods and applied to other high musculoskeletal-disorders-risk trades.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".