Upper extremity pain and computer use among engineering graduate students: A replication study
Bibliographic record
Abstract
BACKGROUND: Recent literature identified upper extremity musculoskeletal symptoms at a prevalence of >40% in college populations. The study objectives were to determine weekly computer use and the prevalence of upper extremity musculoskeletal symptoms in a graduate student population, and make comparisons with previous graduate and undergraduate cohorts. METHODS: One hundred sixty-six graduate students completed a survey on computing and musculoskeletal health. Associations between individual factors and symptom status, functional limitations, academic impact, medication use, and health services utilization were determined. Logistic regression analyses evaluated the association between symptom status and computing. Cross-study comparisons were made. RESULTS: More symptomatic participants experienced functional limitations than asymptomatic participants (74% vs. 32%, P < 0.001) and reported medication use for computing pain (34% vs. 10%, P < 0.01). More participants who experienced symptoms within an hour of computing used health services compared to those who experienced symptoms after an hour of computer use (60% vs. 12%, P < 0.01). Years of computer use (OR = 1.59, 95% CI 1.05-2.40) and number of years in school where weekly computer use was more than 10 hr (OR = 1.56, 95% CI 1.04-2.35) were associated with pain within an hour of computing. Cross-study comparisons found college populations more similar than different. CONCLUSION: The overall findings reinforced previous literature documenting the prevalence of upper extremity musculoskeletal symptoms in college populations, suggesting an important population for participating in public health interventions designed to support healthy computing practices and identify risk factors important to evaluate in future cohort studies. Am. J. Ind. Med. 52:113-123, 2009. (c) 2008 Wiley-Liss, Inc.
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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 | 0.007 | 0.011 |
| 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".