Respiratory Symptoms Associated with Workplaces Located along a Road with High-Traffic at Cotonou, Benin
Bibliographic record
Abstract
Objective: Air pollution is becoming one of the public concerns requiring urgent feasible response per local context. Defining accurately the level of exposure of outdoor air pollution effect on health of venders working along main roads and roundabouts with high traffic is important. This study was conducted to assess respiratory health risks on venders associated with different geographical positions in Cotonou. Methods: A cross-sectional study was conducted along the main road with high traffic including three roundabouts in Cotonou. The 194 study participants from all shops, one respondent per shop, were given a unique GPS data associated to indicate the shop’s geographical position. The study employed validated questionnaire on respiratory symptoms. Results: Study participants had mean age of 36.26 (±11.65) years with sex ratio of (M/F) 1.8. Majority (72.7%) of study participates reported to have at least one respiratory symptom and 69% of them were working in non-ventilated rooms. The proportion of having at least one respiratory symptom was significantly different (p < 0.001) between the subpopulations at roundabouts (96.15%) and apart from the roundabouts (64.08%). The most reported symptoms were cough (47.94%) and itchy nose (42.27%). The risk of cough (OR = 5.15; 95% CI = [2.21 - 12.04]; p < 0.001) and itchy nose symptoms (OR = 3.44; 95% CI = [1.55 - 7.63]; p = 0.002) were higher in individuals working at the roundabouts compared to those working along the main road. Conclusion: Working in roundabout is associated with more respiratory symptoms than working in shops along main road. Air pollution mitigation efforts should focus in such settings, taking into account the sub-population of workers in resource limited countries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".