Management of Hand Screen Printing Workers Health Using Ergonomic Design Intervention
Bibliographic record
Abstract
Hand Screen Printing (HSP) is one of the dominant textile-processing industries that plays a significant role in the employment of people in and around the rural regions of the developing countries. Further, most of the HSP industries are small scale industries sector, workers are illiterate or lack work safety knowledge, also both management and workers ignore the ergonomic issues in their day to day life. Though various research groups have attempted to evaluate both qualitatively and quantitative the ergonomic issues faced by HSP workers, due to lack of direct solution to improve the HSP workers’ ergonomic environment, there is still a huge space is available for further intervention. The present study aims to redesign the squeegee frame for HSP workers and validate its effect over the workers’ health. In this study, the traditional squeegee was modified by considering the evaluated ergonomic issues. To evaluate the design intervention, ten male volunteers participated in the study and performed the HSP operation using both traditional squeegee (pre-intervention) and retrofitted squeegee (post-intervention). Further, the study population was divided into two equal group such as control group and low back pain (LBP) group. The surface Electromyogram (sEMG) signal, Rapid Entire Body Assessment (REBA) score and Borg’s scale data were collected, to assess the post intervention attempt. Result inferred that retrofitted squeegee intervention had reduced the REBA score (postural risk) by 52% and Borg’s scale by 51% (workers’ pain intensity). In addendum, sEMG study supported these findings by showing a decline in muscle fatigue for the LBP group. This study concludes that, retrofitted squeegee intervention aspect can be an effective agent to improve the ergonomic posture and reduce the musculoskeletal disorders among HSP workers and significantly increase the productive of textile industry.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".