A look through Latin America truck drivers’ health, a systematic review and meta-analysis
Bibliographic record
Abstract
Heavy truck drivers represent a social group of great importance to any country's economy. Their professional activity requires a high level of dedication. Due to the irregular hours in their work routine and adopted habits, they mostly predispose them to a diversity of health problems. The purpose of this study is to perform a systematic review and meta-analysis aiming to identify the prevalence of diabetes, hypertension, and obesity in Latin American Truck Drivers. We searched the PubMed, Web of Science, Scopus and LILACS databases, for scientific publications articles, as reported by The PRISMA Statement. From 1,382, 7 studies were included according to the established criteria. The hypertension prevalence found was 34.2%, diabetes was of 9.2% and the highest prevalence found was for overweight and obesity (56%). Meta-analysis presented that drivers have a higher prevalence of overweight or obesity when compared to eutrophic individuals and that drivers with diabetes and hyperglycemia have a lower prevalence. Due to their work activity, their access to the health system is compromised limiting any type of monitoring of their health. This study showed that there is, in Latin America, an investment and assistance gap, both in the health sector and in the research section, for this professional category, which is so important to the economy of these countries. These data should help to identify the difficulties faced by this professional in health assistance, road safety, public safety, leisure and social life. This research also highlighted that they are young and already have the first sign of non-transmissible chronic diseases, which is overweight and obesity.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".