A multi-method study evaluating computing-related risk factors among college students
Bibliographic record
Abstract
PURPOSE: To characterize undergraduate computer use using different data collection methods, emphasizing computing-related postures, use patterns and upper extremity musculoskeletal symptoms. SUBJECTS AND METHODS: In Spring, 2004, undergraduate students from a single dormitory at a private university agreed to complete a College Computing & Health Survey. For three separate data collection periods each lasting a week, we observed postures during computer once per period and continuously measured computer input device usage. During these three periods, students self-reported computer usage and symptoms 3-5 times daily. RESULTS: Thirty students participated and all completed the study. Eighty-six percent reported ever experiencing symptoms after computer work. There were no time-related trends across data collection periods for posture, symptoms, and computing activities and patterns. Typed work and communicating (when compared with playing games) were usually the predominant computing activities throughout the semester. There was significantly greater self-reported computer use than that directly measured (p<0.05). CONCLUSION: This is the first study that utilized several methods of exposure assessment to describe computing postures, use patterns and upper extremity musculoskeletal symptoms among a college student cohort. Epidemiological studies need to explore time-related changes such as time of day, weekday, and days into the semester to further understand symptoms, posture, and computer use changes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".