Reconstructing the final narrative: <i>how psychological autopsies reconstruct the final narratives of youth across diverse sociocultural context</i>
Bibliographic record
Abstract
Youth suicide is a leading cause of death worldwide, with Indigenous and marginalized youth disproportionately affected. While traditional psychological autopsy studies tend to emphasize psychiatric diagnoses, they often fail to capture the narrative, cultural, and existential dimensions that shape suicidality. This study draws on 21 psychological autopsy investigations of youth aged 10 to 25, using narrative identity theory, existential suicidology, and models such as McAdams’ narrative identity theory and Galynker’s Suicide Crisis Syndrome to explore the deeper contours of suicidal risk. The findings suggest that suicide in youth frequently follows a trajectory marked by cumulative adversity, fractured identity, and ruptured relationships. Common themes included a loss of belonging, untreated psychological pain, cultural disconnection, and the foreclosure of personal narrative. Many youth had encountered healthcare services but remained unseen or unsupported in ways that addressed the meaning-making and relational aspects of their distress. Rather than viewing suicide solely as a psychiatric outcome, the study proposes reframing it as a breakdown in narrative coherence and existential grounding. A more humane and effective approach to prevention and care will require integrating narrative-informed, culturally sensitive frameworks into early intervention, postvention, and mental health systems more broadly.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".