The French ecology of medical care. A nationwide population-based cross sectional study
Bibliographic record
Abstract
PURPOSE: Studies in the United States, Canada, Belgium, and Switzerland showed that the majority of health problems are managed within primary health care; however, the ecology of French medical care has not yet been described. METHODS: Nationwide, population-based, cross sectional study. In 2018, we included data from 576,125 beneficiaries from the General Sample of Beneficiaries database. We analysed the reimbursement of consultations with (i) a general practitioner (GP), (ii) an outpatient doctor other than a GP, (iii) a doctor from a university or non-university hospital; and the reimbursement of (iv) hospitalization in a private establishment, (v) general hospital, and (vi) university hospital. For each criterion, we calculated the average monthly number of reimbursements reported on 1,000 beneficiaries. For categorical variables, we used the χ2 test, and to compare means we used the z test. All tests were 2-tailed with a P-value < 5% considered significant. RESULTS: Each month, on average, 454 (out of 1,000) beneficiaries received at least 1 reimbursement, 235 consulted a GP, 74 consulted other outpatient doctors in ambulatory care and 24 in a hospital, 13 were hospitalized in a public non-university hospital and 10 in the private sector, and 5 were admitted to a university hospital. Independently of age, people consulted GPs twice as much as other specialists. The 13-25-year-old group consulted the least. Women consulted more than men. Individuals covered by complementary universal health insurance had more care. CONCLUSIONS: Our study on reimbursement data confirmed that, like in other countries, in France the majority of health problems are managed within primary health care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".