Déterminants de l’utilisation des services médicaux en régime d’assurance-maladie
Bibliographic record
Abstract
Under the regime of public health insurance, the utilization of health cares are determined by various socio-demographic and economic characteristics of the beneficiaries. These determinants are estimated in this study where we apply the dummy variable regression technique to the AMULET data bank, a 1971 cross-section of 8,608 beneficiaries in the province of Québec where most of health cares are free. In increasing order of importance, we find that : i) The individual utilization of health care is increasing with the age of beneficiaries and is higher for women than for the men for the age-group 15-50 years. This tendency is reversed for the age-group 50 years and older since the rate of increase in utilization is higher for men. There is, however, any significant difference in utilization on the basis of sex discrimination for the age-group 0-15 years. The structure age-sex, being of course a proxy of the health status of the beneficiaries, is the most important determinant of health cares utilization. ii) Individual utilization depends on the income class to which belongs the beneficiary. The beneficiaries of the highest and the lowest income class utilize more health care than those belonging to the so called "middle class". Notice however that the lowest income class in the data sample is composed in majority of aged beneficiaries. iii) The size of the beneficiaries' family is not a significant determinant of the utilization of health care for children of age-group 0-15 years. For other age-group however, utilization decreases with this family size for men, but increases for women. iv) The geographic area where the beneficiaries are identified is a weak determinant of utilization. Beneficiaries in urban area utilize more of health care than those living in rural area.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".