ERGONOMIC EDUCATION – A TOOL TO MAINTAIN HEALTH
Bibliographic record
Abstract
Objective : To identify the occurrence of leg pain amongst computer users and assess its relation to age, sex, occupation and duration of computer use. Methodology : It was a cross sectional study conducted from January till December 2011. A self reported questionnaire tailored with Occupational Health and Safety Act of the Ministry of Labor, Ontario, Canada was used. Participants were randomly selected; responses analyzed by SPSS software version 15. Chi square test was applied to results and considered significant with p value <0.05 Results : A total of 416 participants responded with mean age of 34.87±8.78 years. There were 231(55.5 %) males. Out of 416, 123(29.5%) participants had work related leg symptoms [66(15.8%) male and 57 (13.7%) female]. Occurrence of leg pain within one to two hours of consecutive work was significantly more in 26-35 and 36-45 year age groups. Postural changes incorporated through frequent short breaks improved leg symptoms in between eight to nine out of ten participants (104/123). The improvement was significantly more in 26-35 and 36-45 year age groups. Leg symptoms showed no relation with the length of computer usage or daily usage or between both sex and working groups. Conclusion : Leg pain/tingling/numbness is an early sign of repetitive injuries that can be timely addressed by ergonomic education and improving postural health through short breaks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".