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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".