Life-History Factors Influence Teenagers’ Suicidal Ideation: A Model Selection Analysis of the Canadian National Longitudinal Survey of Children and Youth
Bibliographic record
Abstract
Suicidality is an important contributor to disease burden worldwide. We examine the developmental and environmental correlates of reported suicidal ideation at age 15 and develop a new evolutionary model of suicidality based on life history trade-offs and hypothesized accompanying modulations of cognition. Data were derived from the National Longitudinal Survey of Children and Youth (Statistics Canada) which collected information on children’s social, emotional, and behavioral development in eight cycles between 1994 and 2009. We take a model selection approach to understand thoughts of suicide at age 15 ( N ≈ 1,700). The most highly ranked models include social support, early life psychosocial stressors, prenatal stress, and mortality cues. Those reporting consistent early life stress had 2.66 greater odds of reporting thoughts of suicide at age 15 than those who reported no childhood stress. Social support of the primary caregiver, neighborhood cohesion, nonkin social support of the adolescent, and the number of social support sources are all associated with suicidal thoughts, where greater neighborhood cohesion and social support sources are associated with a reduction in experiencing suicidal thoughts. Mother’s prenatal smoking throughout pregnancy is associated with a 1.5 greater odds of suicidal thoughts for adolescents compared to children whose mother’s reported not smoking during pregnancy. We discuss these findings in light of evolutionary models of suicidality. This study identifies both positive and negative associations on suicidal thoughts at age 15 and considers these in light of adaptive response models of human development. Findings are relevant for mental health policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".