Road traffic delays and musculoskeletal health complaints among full-time bank employees: A cross-sectional study in Dhaka city
Bibliographic record
Abstract
Abstract Background The factors of road traffic delays (RTDs) have significant consequences for both commuters’ health and the country’s economy as a whole. Addressing the musculoskeletal health complaints (MHCs) among full-time employees has not been fully explored. The current study investigates the association between RTDs-related factors and MHCs among bank employees. Methods We conducted a cross-sectional study among full-time employees from 32 banks in Dhaka city. Descriptive statistics summarized the gaps in the socio-demographic and RTDs-related factors on the one-month prevalence of MHCs. Random intercept logistic regression models were used to identify the associate factors of the MHCs. Results Out of 628 full-time bank employees, the one-month prevalence of MHCs was 57.7%. The MHCs are more prevalent among adults of age group 40-60 years (68%) than the age group 20-40 years (54%). The one-month prevalence of lower back pain (LBP) was highest (36.6%), followed by neck pain (22.9%) and upper back pain (21.2%). Multilevel logistic regression analysis of employees showed that the odds of MHCs were lower among male employees (AOR=0.42, 95% CI= 0.27, 0.64), car commuters (AOR = 0.38, 95% CI=0.19-0.76, reference: bus commuters) and rickshaw commuters (AOR=. = 0.39, 95% CI=0.22-0.69, reference: bus commuters). The MHCs were significantly higher among employees with following factors: obesity (AOR= 1.50, 95% CI= 1.02-2.21), prolonged commute time to the office (AOR = 7.48, 95% CI =3.64-15.38) and working extended-time in a day (AOR= 1.50, 95% CI= 1.02-2.21). Conclusions The study indicates a high burden of musculoskeletal health complaints among the employees in Dhaka city, and the most prevalent complaint was low back pain. Our study suggests that factors related to road traffic delays might act synergistically on developing musculoskeletal problems in full-time employees.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".