Undergraduate college students' upper extremity symptoms and functional limitations related to computer use: A replication study
Bibliographic record
Abstract
PURPOSE: To replicate Hupert et al.'s [5] evaluation of computer-related upper extremity musculoskeletal symptoms, functional limitations, academic performance impact, medication use and health services utilization among a college student population. SUBJECTS AND METHODS: A cross-sectional survey of undergraduate students living in a single residential dormitory at a private southwestern university who agreed to participate completed the College Computing & Health Survey in the Spring of 2001. RESULTS: Of the 127 dormitory residents, 116 students participated and 54% reported experiencing symptoms associated with computer usage. Sixty-two percent of students surveyed experienced functional limitations. More women than men reported symptoms, functional limitations and neck and shoulder pain; those with functional limitations reported higher use of medications than other participants. CONCLUSION: These findings, which match the general findings of the previous research study conducted with the same instrument but in a different college student population, suggest a high level of computer-use-related musculoskeletal symptoms among college students. Universities may want to consider providing ergonomic training designed for college students to: conduct workstation assessments; identify computer related problems (risk factors); and, propose ergonomic solutions.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".