Developmental model of suicide trajectories
Bibliographic record
Abstract
BACKGROUND: Most developmental studies on suicide do not take into account individual variations in suicide trajectories. AIMS: Using a life course approach, this study explores developmental models of suicide trajectories. METHOD: Two hundred and fourteen suicides were assessed with mixed methods. Statistical analysis using combined discrete-time survival (DTS) and growth mixture modelling (GMM) generated various trajectories, and path analysis (Mplus) identified exogenous and mediating variables associated with these trajectories. RESULTS: Two groups share common risk factors, and independently of these major risk factors, they have different developmental trajectories: the first group experienced a high burden of adversity and died by suicide in their early 20s; and the second group experienced a somewhat moderate or low burden of adversity before they took their own life. Structural equation modelling identified variables specific to the early suicide trajectory: conduct and behavioural difficulties, social isolation/conflicts mediated by school-related difficulties, the end of a love relationship, and previous suicide attempts. CONCLUSIONS: Psychosocial adversity between 10 and 20 years of age may warrant key periods of intervention.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".