The Effect of Two Office Ergonomics Field Interventions and their Replication on Visual Symptoms
Bibliographic record
Abstract
Upper extremity musculoskeletal symptoms and disorders in the workplace continue to be a significant public health burden (National Research Council and Institute of Medicine, 2001). These outcomes, as they relate to computer use, are important as greater than 50% of employed adults in the United States use a computer at work (U.S. Census Bureau, 2003). Visual symptoms are also known to often coexist with upper extremity musculoskeletal symptoms with computer workers. This study examines the effect of two office ergonomic interventions, a highly adjustable chair and an office ergonomics training, on the reduction of specific visual symptoms. Additionally, findings from a replicated study site are compared with those of the original study. Multilevel logistic regression models were used to estimate the effect. In comparing the original and replication worksites we find specific visual symptom reductions occurred for the group receiving both the chair with training at the original and replicated worksite. However, the group receiving only the office ergonomics training experienced individual visual symptom reductions at the replication worksite but not the original worksite. There were differences in the specific visual symptoms affected by the office ergonomics interventions when comparing worksites.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".