Factors associated with past-year medication use and psychotherapy in adults with suicidal ideation in France
Bibliographic record
Abstract
Background: The objective of the study is to assess the sociodemographic and clinical factors associated with past-year medication use and/or psychotherapy among adults with suicidal ideation in the past 12 months. Methods: Data were drawn from the 2017 Health Barometer survey, a large computer-assisted telephone survey on a representative sample of the general population aged 18 to 75 years living in metropolitan France ( n = 25,319). Logistic and multinomial regression analyses were used to study past-year medication use and/or psychotherapy as a function of sociodemographic and clinical factors. Analyses were restricted to individuals reporting suicidal ideation in the past year ( n = 1,148). Results: Overall, 43.6% of adults with suicidal ideation reported no treatment for a mental health reason in the past year; 36.6% reported using medication only, 4.8% psychotherapy only, and 15.0% both. Sociodemographic and clinical factors associated with increased probability of treatment varied as a function of the type of treatment received. Adjusting for key factors including clinical factors, older adults with suicidal ideation were more likely than younger adults to receive medication only. Conclusions: The findings point to differential inequalities in access to medication and psychotherapy among adults with suicidal ideation in the general population of France.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".