Primary health care needs for a priority population: A survey of professional truck drivers
Bibliographic record
Abstract
BACKGROUND: There are no Canadian data regarding health and wellness of transport truck drivers. OBJECTIVES: We pilot-tested a survey instrument to examine the risk factors and health needs of Canadian truck drivers. METHODS: A self-administered survey was completed by truck drivers employed in 13 companies in-and-near Hamilton, Ontario, Canada. The survey was developed using published tools with input from focus groups and included demographics, health issues, health service utilization, and awareness of workplace health programs. Descriptive statistics were used to estimate prevalence of health issues and risk factors. RESULTS: 822 surveys were distributed and 406 drivers (49.4%) responded; 48.5% were 50 years and older, 96.0% were male. Diabetes, heart disease, stroke, arthritis, and lung problems were reported by 7%, 4.1%, 0.6%, 10.8% and 2.8% respectively. 96% had salt intake above the recommended daily intake, 31.5% smoked daily and the prevalence of being overweight and with poor diet was 53.2% and 48.4%. CONCLUSIONS: Prevalence of current disease was low; however, prevalence of risk factors for chronic disease was substantial. The survey was feasible to administer and provided benchmark data regarding truck drivers' perceived health. A national survey of Canadian drivers is suggested to improve generalizability and facilitate analysis for associations to poorer driver health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".