Observance des conseils de prévention et de soins préconisés au décours de l'Examen de Prévention en Santé : l’expérience au Centre d’Examens de Santé de Bayonne
Bibliographic record
Abstract
Introduction: in an increasing social inequalities environment concerning health in France, access to healthcare for vulnerable population is a major health issue. The Health Insurance Health Examination Centers (CES) have for mission to improve that access by offering Preventive Health Examinations (EPS) to precarious populations. Our study’s main objective was to determine the ratio of consultants who performed examinations and cares recommended by the EPS. Method: cohort study with retrospective, single-center data collection within the Bayonne CES with consultants who carried out an EPS in 2018. Data collected from CES medical software and then from CPAM software. Results: 1459 consultants were included in the study. At least one anomaly was detected for 86.02% of those, among whom 23.11% (minimum workforce) did not start any care after the EPS. Typical profile of the patient belonging to the non-responder sub-group is : a young man, without neither appointed regular general practitioner nor complementary health insurance. Anomaly type, its severity and actions’ implementation within the CES influenced the follow-up. Discussion: EPS allows vulnerable and far from care population to have access to preventive actions, screening and to ease identified anomalies’ management, allowing them to better integrate a care pathway. However, almost a quarter of consultants do not follow up on the preventive examination done.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".