The Rapid Office Strain Assessment (ROSA): Validity of online worker self-assessments and the relationship to worker discomfort
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of this study was to determine if office workers were capable of using an online version of the Rapid Office Strain Assessment (ROSA) tool to accurately assess musculoskeletal disorder risk factors in their own offices, and see if online training can reduce worker-reported discomfort. Fifty-five participants completed a four week program where they assessed their own office simultaneously with a trained observer, and either received or did not receive feedback on their performance. Significant differences were found between worker- and observer-reported ROSA final scores, and for the mouse and keyboard section, with workers underestimating these risk factors on average, compared to the trained observer. Worker and observer assessments of the chair, monitor and telephone were not significantly different but were significantly correlated (R values of 0.60 and 0.48). There were a greater number of significant correlations between worker-reported ROSA final scores and total body discomfort (3 instances) compared to observer-reported relationships (1 instance). Feedback appeared to have a detrimental effect on worker-assessment accuracy, and the relationship between discomfort and ROSA scores. Mean discomfort decreased across the four weeks of the study (up to a 51.6% decrease), as did ROSA final scores (3.9 to 3.5). Additional work is required to improve the validity of worker-reported scores in all sections of ROSA, but self-assessments of office workstations using the current ROSA online application do show promise in terms of assisting workers to decrease risk factors related to musculoskeletal disorders, and decrease discomfort levels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it