Early Predictors of Suicidal Ideation in Young Adults
Bibliographic record
Abstract
OBJECTIVE: To identify early predictors of suicidal ideation in young adults, and to determine when specific time-varying determinants become important in predicting later suicidal ideation. METHODS: Data were available for 877 participants in the Nicotine Dependence in Teens study, an ongoing prospective cohort of students aged 12 to 13 years at cohort inception in 1999. Time-invariant covariates included age, sex, mother's education, language, and self-esteem. Time-varying covariates included depression symptoms, family stress, other stress, alcohol use, cigarette use, and team sports. Independent predictors of past-year suicidal ideation at age 20 years were identified in 5 multivariable logistic regression analyses, one for each of grades 7, 8, 9, 10, and 11. RESULTS: Eight per cent of participants (mean age 20.4 years [SD 0.7]; 46% male) reported suicidal ideation in the past year. In grade 7, none of the potential predictor variables were statistically significantly associated with suicidal ideation. In grade 8, participation in sports teams in and (or) outside of school protected against suicidal ideation (OR 0.6; 95% CI 0.4 to 0.8; P = 0.002). Depression symptoms in grades 9, 10, and 11 were independent predictors of suicidal ideation (OR 2.2; 95% CI 1.5 to 3.2, OR 1.6; 95% CI 1.0 to 2.5, and OR 1.9; 95% CI 1.1 to 3.4, respectively). No other variables were statistically significant in the multivariate models. CONCLUSION: Depression symptoms as early as in grade 9 predict suicidal ideation in early adulthood. It is possible that early detection and treatment of depression symptoms are warranted as part of suicide prevention programs.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".