Road traffic delays in commuting workplace and musculoskeletal health among sedentary workers: A cross-sectional study in Dhaka city
Bibliographic record
Abstract
OBJECTIVES: Despite previous research aimed at identifying factors linked to musculoskeletal health issues, there was no evidence about the relationship between road traffic delays (RTDs) and musculoskeletal health in sedentary employees. As a result, the aim of our research was to understand such a correlation among bank employees in Dhaka, Bangladesh. METHODS: A cross-sectional analysis was conducted with bank employees who worked in sedentary settings. The Eriksen subjective health complaints scale was used to measure the eight items of musculoskeletal health complaints (MHCs), and RTDs were measured using principal component analysis using variables commute time, distance, and traffic congestion experience to work. The association between RTDs and MHCs was identified using a multilevel model after adjusting potential confounders. RESULTS: A total of 628 employees (mean[SD] age, 36.1[7.0] years; 254[40.5%] women) participated in the study. Among the employees, the one-month prevalence of MHCs was 57.2%. The highest prevalence of MHCs was low-back pain (36.6%), followed by neck pain (22.9%) and upper-back pain (21.2%). Also, 136(21.7%) employees reported long-RTDs in commuting workplace and 81% of them had MHCs. The multilevel analysis identified that long-RTDs had a significant relationship with MHCs (adjusted odds ratio, AOR = 10.20, 95%CI = 5.41-16.91). Private transportation commuters reported 70% reduced odds of MHCs (AOR = 0.30, 95%CI = 0.15-0.59) and walking or bicycling commuters had 84% fewer MHCs (AOR = 0.16, 95%CI = 0.10-0.28) compared to public bus commuters. CONCLUSIONS: Sedentary employees with long-RTDs reported increased MHCs, emphasizing the importance of including musculoskeletal exercise in office facilities. Findings of this study also highlight the need for a sound public transportation system in Dhaka city.
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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 | 0.001 |
| 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) | 0.002 | 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".