Differences in health care seeking behaviour between rural and urban communities in South Africa
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore possible differences in health care seeking behaviour among a rural and urban African population. DESIGN: A cross sectional design was followed using the infrastructure of the PURE-SA study. Four rural and urban Setswana communities which represented different strata of urbanisation in the North West Province, South Africa, were selected. Structured interviews were held with 206 participants. Data on general demographic and socio-economic characteristics, health status, beliefs about health and (access to) health care was collected. RESULTS: The results clearly illustrated differences in socio-economic characteristics, health status, beliefs about health, and health care utilisation. In general, inhabitants of urban communities rated their health significantly better than rural participants. Although most urban and rural participants consider their access to health care as sufficient, they still experienced difficulties in receiving the requested care. The difference in employment rate between urban and rural communities in this study indicated that participants of urban communities were more likely to be employed. Consequently, participants from rural communities had a significantly lower available weekly budget, not only for health care itself, but also for transport to the health care facility. Urban participants were more than 5 times more likely to prefer a medical doctor in private practice (OR:5.29, 95% CI 2.83-988). CONCLUSION: Recommendations are formulated for infrastructure investments in rural communities, quality of health care and its perception, improvement of household socio-economical status and further research on the consequences of delay in health care seeking behaviour.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".