Uptake of health services among truck drivers in South Africa: analysis of routine data from nine roadside wellness centres
Bibliographic record
Abstract
BACKGROUND: Long-distance truck drivers are occupationally susceptible to poor health outcomes. Their patterns of healthcare utilisation and the suitability of healthcare services available to them are not well documented. We report on truck driver healthcare utilisation across South Africa and characterise the client population of the clinics serving them for future service development. METHODS: We analysed anonymised data routinely collected over a two-year period at nine Roadside Wellness Centres. Associations between services accessed and socio-demographic characteristics were assessed using univariable and multivariable logistic regression models. RESULTS: We recorded 16,688 visits by 13,252 individual truck drivers (average of 1.26 visits/person) who accessed 17,885 services for an average of 1.07 services/visit and 1.35 services/person. The mean age of truck drivers was 39 years. Sixty-seven percent reported being in stable relationships. The most accessed services were primary healthcare (PHC)(62%) followed by HIV (32%). Low proportions (≤6%) accessed STI,TB and malaria services. Most visits were characterised by only one service being accessed (93%, n = 15,523/16,688). Of the remaining 7% of visits, up to five services were accessed per visit and the combination of TB /HIV services in one visit remained extremely low (<1%, n = 14/16,688). Besides PHC services at the beginning of the reporting period, all service categories displayed similar seasonal utilisation trends(i.e. service utilisation peaked in the immediate few months post clinics opening and substantially decreased before holidays). Across all service categories, younger truck drivers, those with a stable partner currently, and those of South African origin were the main clinic attendees. Older truck drivers (≥40 years) were more likely to access TB and PHC services, yet less likely to access HIV and STI services. Those with stable partners were less likely to access STI and TB services but more likely to access malaria and PHC services. South African attendees were more likely to access PHC, while attendees from other nationalities were more likely to access HIV and malaria services. CONCLUSIONS: This utilisation analysis shows that tailored services assist in alleviating healthcare access challenges faced by truck drivers, but it underscores the importance of ensuring that service packages and clinics speak to truck drivers' needs in terms of services offered and clinic location.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".