An Ounce of Discretion Is Worth a Pound of Wit — Ergonomics Is a Healthy Choice
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to identify the occurrence and outcome of low back ache amongst computer users and their relation to age, gender, occupation and duration of computer use. MATERIALS AND METHODS: A self reported questionnaire tailored from Occupational Health and Safety Act of the Ministry of Labor, Ontario, Canada was used. RESULTS: 416 participants 55.5% males and 45% females using computers for a minimum of five years with age range 22 to 59 years belonged to different occupational groups. Consecutive hours of computer work was found to be associated with work related backache or discomfort in 27.4% (n = 114) participants (16.1% male, 11.3% female). Frequent short breaks improved backache (p value <0.001) in 93 (22.4%) participants (13.2% male, 9.2% female). No significant relation was observed with the duration of computer usage or usage per day; between the two genders or occupational groups. Backache had no significance within age groups. CONCLUSION: Our study identifies the occurrence of low back pain among those who are using computer for consecutive hours without breaks and the results suggest the need to create health awareness especially use of short breaks to minimize the risk and occurrence of low back pain. The result of this study can also be used to improve ergonomic design and standards.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".