Risk Factors for Musculoskeletal Symptoms among Call Center Operators of a Bank in Sao Paulo, Brazil
Bibliographic record
Abstract
OBJECTIVE: Identify risk factors for musculoskeletal symptoms among call center operators of a bank in São Paulo, Brazil. METHODS: Ergonomic work analysis was carried out, involving work observation and interviews. Self-answered questionnaires performed by 108 call center operators. RESULTS: Women represented 88% of the call center operators, 70% of them were in the age bracket of 18 to 23 yr. Daily working time was 6-h with one 30 min break. Workers remained seated 95% of the time, typing and answering telephone calls. Men' s work consisted of more active telemarketing and women's of customer services. Among female operators the prevalence of neck/shoulder symptoms was 43% (95% CI, 33-53) and of wrist/hand was 39% (95% CI, 29-49). Risk factors associated with wrist/hand symptoms were: inadequate height of table (Odds ratio (OR) 3.67, 95% CI, 1.12-11.96) and to answer above 140 calls/d (OR 3.36, 95% CI, 1.16-9.71). Risk factors associated with neck/shoulder symptoms were making fewer rest breaks (OR 3.17, 95% CI, 1.11-8.97) and inadequate thermal comfort (OR 3.06, 95% CI, 1.09-8.62). CONCLUSIONS: Prevention of musculoskeletal disorders among call center operators requires an integrated approach including improved workstation design, thermal comfort environment, well-scheduled work-rest regime and realistic production goals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".