Evaluation of two posture survey instruments for assessing computing postures among college students
Bibliographic record
Abstract
OBJECTIVE: To determine agreement between two posture assessment survey instruments and which, if any, were correlated with experiencing upper extremity musculoskeletal symptoms. METHODS: Thirty undergraduate participants had three postural assessment surveys completed, one each for three separate 7-day data collection periods during a semester. Two observation assessment tools were used, a modified Rapid Upper Limb Assessment (mRULA) for computer users for the right and left limbs and the University of California Computer Use Checklist. Concurrently, upper extremity musculoskeletal symptom experience paired to each postural assessment was measured. Lin's concordance correlation coefficient evaluated survey agreement and multi-level statistical models described associations between survey responses and symptoms. RESULTS: There was no agreement between the two postural assessment tool scores (p> 0.85). In adjusted models, the UC Computer Use Checklist was positively associated with symptoms occurrence (OR=1.4, 90% CI 1.2-1.6 for any symptoms; OR=1.3, 90% CI 1.0-1.6 for moderate or greater symptoms). Associations with mRULA scores were inconsistent in that they were sometimes protective and sometimes indicators of risk, depending on the covariates included in the models. CONCLUSION: The mRULA for computer users and the UC Computer Use Checklist were independent of each other; however, due to the inconsistent associations with symptoms we cannot conclude one instrument is superior to the other. Our data do suggest the UC Computer Use Checklist demonstrates a traditional relationship with symptoms, where increasing scores signifiy greater risk. We observed a nontraditional relatioship with symptoms for the mRULA for computer users that needs to be further examined. This is a pilot study and, thus, findings should be interpreted as exploratory. Associations observed in the current study will be used to test hypotheses in the cohort study recently conducted.
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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 | 0.021 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".