Natural history of suicidal behaviors in a population-based sample of young adults
Bibliographic record
Abstract
BACKGROUND: Suicidal behaviors in young individuals represent an important public health problem. Understanding their natural history and relationships would therefore be of clinical and research value. In this study, we examined the natural histories of several suicidal behaviors and investigated two conceptual models of suicidality (dimensional and categorical) in the context of adolescent and adult-onset suicide attempts. METHOD: Participants were members of a prospectively studied, representative, population-based school cohort followed since age 6 (n = 3017) through mid-adolescence (n = 1715) to their early twenties (n = 1684). Outcome measures included suicidal ideation, attempts and completions. RESULTS: Approximately one in 500 individuals died by suicide. About 33% had suicidal ideas and 9.3% made at least one suicide attempt. Over half (4.9%) of the self-reported attempters made their first attempt before age 18. With the exception of current suicidal ideas, non-fatal suicidal behaviors were more prevalent in females. In general, parental and cross-sectional self-reports underestimated suicidality rates. Aikaike (AIC) and Bayesian (BIC) information criteria suggested the ordinal model, and dimensional conceptualization of suicide attempts of different onset age, to be more optimal than its multinomial/categorical counterpart (ordinal: AIC 567.55, BIC 635.67; multinomial: AIC 616.59, BIC 723.83). Both models, nevertheless, identified five common factors of relevance to suicidal diathesis: gender, disruptive disorders, childhood anxiousness and abuse, and suicidal thoughts. CONCLUSIONS: Non-fatal suicidal behaviors in adolescents and young adults are more common than suggested by cross-sectional studies and parental reports. The dimensional model may be more useful in explaining the relationship of suicide attempts of different age of onset.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".