Primary care in Germany: access and utilisation—a cross-sectional study with data from the German Socio-Economic Panel (SOEP)
Bibliographic record
Abstract
OBJECTIVES: (1) To describe the accessibility of general practitioners (GPs) by the German population; (2) to determine factors on individual and area level, such as settlement structure and area deprivation, which are associated with the walking distance to a GP; and (3) to identify factors that may cause differences in the utilisation of any doctors. DESIGN: Cross-sectional study using individual survey data from the representative German Socio-Economic Panel (SOEP) linked with area deprivation data from the German Index of Multiple Deprivation for 2010 (GIMD 2010) and official data for settlement structure (urban/rural areas) at district level. Logistic regression models were estimated to determine the relationship of individual and area factors with the distance to a GP. Negative binomial regressions were used to analyse the association with utilisation. SETTING: Germany. POPULATION: n=20 601 respondents from the SOEP survey data 2009. PRIMARY OUTCOME MEASURE: Walking distance to a GP. SECONDARY OUTCOME MEASURE: Doctor visits. RESULTS: Nearly 70% of the sample lives within a 20 min walk to a GP. People living in the most deprived areas have a 1.4-fold (95% CI 1.3 to 1.6) increased probability of a greater walking distance compared with the least deprived quintile, even after controlling for settlement structure and individual factors. In rural districts, people have a 3.1-fold (95% CI 2.8 to 3.4) higher probability of a greater walking distance compared with those in cities. Both area deprivation and rurality have a negative relationship with the utilisation of physicians, whereas the distance to a GP is not associated with the utilisation of physicians. CONCLUSION: Walking distance to a GP depends on individual and area factors. In Germany, area deprivation is negatively correlated with the accessibility of GPs while controlling for settlement structure and individual factors. Both area factors are negatively associated with the utilisation of doctors. This knowledge could be used for future GP requirement plans.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".