Incidence and Course of Suicidal Ideation and Suicide Attempts in the General Population
Bibliographic record
Abstract
OBJECTIVE: Suicidal ideation and suicide attempts are important indicators of extreme emotional distress. However, little is known about predictors of onset and course of suicidality in the general population. Our study tried to fill this gap by analyzing data from a prospectively followed community sample. METHOD: Data were derived from the Netherlands Mental Health Survey and Incidence Study (NEMESIS), a 3-wave cohort study in a representative sample (n = 4848) of the Dutch adult general population. RESULTS: The 3-year incidence of suicidal ideation and suicide attempts was 2.7% and 0.9%, respectively. Predictors of first-onset suicidal ideation and suicide attempts were sociodemographic variables (especially the negative change in situation variables), life events, personal vulnerability indicators, and emotional (mood and anxiety) disorders. Comparison of the corresponding odds ratios and confidence intervals revealed that predictors for first-onset suicidal ideation and suicide attempts did not differ significantly. One of the strongest predictors of incident suicide attempts was previous suicidal ideation. Regarding the course of suicidal ideation, it was found that 31.3% still endorsed these thoughts and 7.4% reported having made a suicide attempt 2 years later. CONCLUSIONS: Similar predictors were found for first-onset suicidal ideation and suicide attempts. This suggests that suicidal behaviours may be ordered on a continuum and have shared risk factors. While suicidal thoughts may be necessary for, they are not sufficient predictors of, suicidal acts. The course of suicidality in the general population can be characterized by a minority of people having suicidal experiences that develop over time with progressively increasing severity.
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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 | 0.000 | 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".