Access to general practitioner services: the disabled elderly lag behind in underserved areas
Bibliographic record
Abstract
BACKGROUND: Several studies have shown that people living in areas underserved in physicians have reduced odds of consulting. However, beyond the magnitude of this effect averaged for the whole population, policymakers need to know whether specific subgroups faced with transportation difficulties, such as the elderly and especially the disabled elderly, have a particularly restricted access to physicians when residing in underserved areas. METHODS: The study sample, representative of the French population aged 18-75 in 1999, comprised 12 405 individuals. Multilevel Poisson models were used to investigate the impact of the area-level density of general practitioners (GPs) on the number of GP consultations reported over the previous 12 months. RESULTS: The mean number of GP consultations over the previous 12 months was 3.8 (S.D.=4.9). Multivariate analyses indicated that living in areas underserved in GPs lead to a greater reduction in primary care utilization for the elderly, and especially for the disabled elderly, than for younger age groups. The disabled elderly had 244% more GP consultations (95% CI:+79%, +562%) when they lived in areas with high versus low GP density (defined with the 10th and 90th percentiles as cut-offs). CONCLUSION: If further research confirms our findings, this increasingly disturbing public health issue in industrialized countries where populations are ageing will require priority policy measures. Ensuring that elderly people living in underserved areas have adequate access to primary care may prevent future hospitalizations, use of home care services and institutionalization.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".