Respiratory Health Problem among the Taxi Drivers of Pokhara Metropolitan City, Nepal
Bibliographic record
Abstract
Respiratory health problems affect the respiratory tract and lungs. WHO states that four major potentially fatal respiratory problems will account for about one in five deaths worldwide by 2030. Taxi drivers are among the major sub-population at the risk of respiratory problems because of their exposure to polluted environment. Therefore, the present study aimed to find out the respiratory health problems among the taxi drivers of Pokhara metropolitan city of Nepal. A cross sectional study was conducted among 203 taxi drivers of the Pokhara Metropolitan city. Multistage sampling method was used to select the desired number of taxi drivers. Data were entered into EPI-DATA 3.1 and then analyzed in SPSS 20. Percentage, mean, and standard deviation were assessed to describe, and chi square test was used to infer the findings. Ethical approval for this study was obtained from Nepal Health Research Council. All samples were male with mean age of 38.46 ± 7.8 years. Majority of the taxi drivers were educated up to secondary level (54.2%), married (91.6%), were married and 78.8 percent had income of NRs 1000-1500/day. A large proportion of the drivers (96.6%) had to work for more than 10 hours/day and three-quarters (74.4%) of them did not take rest even in weekends. Nearly a quarter (24.1%) of them complained at least one respiratory health problem or symptom. Prevalence of respiratory health problems among the taxi drivers was 24.1 percent. Job duration was significantly associated with the respiratory symptoms. Key words : Respiratory problem, preventive practice, taxi driver
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".