Risk factors for persistent elbow, forearm and hand pain among computer workers
Bibliographic record
Abstract
OBJECTIVES: This study examined the influence of work-related and personal factors on the prognosis of "severe" elbow, forearm, and wrist-hand pain among computer users. METHODS: In a 1-year follow-up study of 6943 computer users, 673 (10%) participants reported "quite a lot" or more trouble due to elbow, forearm, or wrist-hand pain during the 12 months preceding the baseline questionnaire. Pain status (recovery versus persistence) at follow-up was examined in relation to computer work aspects and ergonomic, psychosocial, and personal factors by questionnaire. In addition, data on objectively recorded computer usage were available for 42% of the participants during the follow-up, measured by means of a program (WorkPaceRecorder) installed on their computers. RESULTS: During the follow-up, two-thirds of the baseline cases improved to some degree, but only one-third experienced substantial improvement. The prognosis was not influenced by mouse or keyboard work (time, speed, micropauses, and average activity periods) or ergonomic workplace conditions. Keyboard times, however, were very low. Pain in other regions was a predictor of persistent arm pain. Except for time pressure, female gender, and type-A behavior, the prognosis seemed independent of psychosocial workplace factors and personal factors. A few cases with severe pain were affected at a level which could be compared to clinical pain conditions. CONCLUSIONS: Our results do not support the hypothesis that computer work activity or ergonomic conditions influence the prognosis of severe arm pain. This result is somewhat surprising and should be tested in other studies. Pain in other regions implies a poorer prognosis for arm pain.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".