Épidémiologie descriptive du risque suicidaire dans le système médical français de médecine générale
Bibliographic record
Abstract
OBJECTIVE: Suicide prevention certainly includes a better knowledge of suicide risk in primary care. A number of international publications have shown interest in assessing this risk, but mostly through specific consultant populations: young patients, old patients, anhedonic, depressive, etc. Our study analyses suicide risk prevalence in patients consulting in general medicine for any somatic or psychiatric reason, their pathology or their age. METHOD: This cross-sectional study was conducted with adult patients (827 subjects included) who were consulting a French generalist doctor panel randomly selected. They filled a validated self-questionnaire (aRSD) assessing their suicide risk in the 15 preceding days and providing professional and personal data. RESULTS: The totally operable 757 files (483 female; 274 male) show that close to a quarter of consultants (24.3%) presents with a positive suicide risk in the 15 days preceding their consultation and 6.3%, reveal a severe risk (aRSD ≥ 7) with ideas and impulses to commit the act. When the reason to consult is psychiatric, 64.6% of these consultants have aRSD positive. One time out of two, the risk is even severe. CONCLUSIONS: This data shows how important the suicide risk prevalence is in general medicine. It confirms the main role played by primary care patricians in acting to prevent suicide risk. This data also shows the contribution represented by a self-questionnaire that would rapidly assess the suicide intent while screening, it.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".