Increases in suicidal thoughts disclosure among adults in France from 2000 to 2021
Bibliographic record
Abstract
BACKGROUND: The objective of the study was to investigate the prevalence of suicidal ideation disclosure over the past two decades in nationally representative samples of the general population, and to identify factors associated with disclosure. METHODS: Data were drawn from consecutive nationally representative cross-sectional Health Barometer surveys. The 2000, 2005, 2010, 2014, 2017, and 2021 waves were pooled to examine disclosure among those who reported 12-month suicidal ideation. Logistic regressions were performed to identify factors associated with the odds of disclosure. RESULTS: Across all waves (n = 124,124), 6014 of adults (4.7 %) reported 12-month suicidal ideation, and among them, 49.7 % talked to someone about it. Disclosure was 39 % in 2000, 44.6 % in 2005, 49.9 % in 2010, 52.8 % in 2014, 47.2 % in 2017, and 64.8 % in 2021. Female gender, a prior suicide attempt, higher education, inactive status, and younger age were associated with significantly greater odds of disclosure. Each survey wave was also associated with a greater likelihood of disclosure when compared to 2000, 1.31 (95 % CI, 1.08-1.59) in 2005, 1.69 (95 % CI, 1.38-2.07) in 2010, 1.89 (95 % CI, 1.52-2.34) in 2014, 1.47 (95 % CI, 1.21-1.79) in 2017, and 2.99 (95 % CI, 2.43-3.68) in 2021. LIMITATIONS: Cross-sectional surveys. CONCLUSIONS: In the general population of France, adults with suicidal ideation were increasingly more likely to disclose their ideation to someone in recent years. Factors associated with odds of disclosure should inform national suicide prevention strategies to identify subgroups who remain less likely than others to disclose their ideation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".