A person-centered approach to studying associations between psychosocial vulnerability factors and adolescent depressive symptoms and suicidal ideation in a Canadian longitudinal sample
Bibliographic record
Abstract
This study used a person-centered approach to identify subgroups of adolescents who are at risk for depression and suicidal ideation. Latent class analysis was first applied to 1,290 adolescents from a Canadian cohort study in order to identify latent vulnerability subtypes based on 18 psychosocial vulnerability factors. Logistic regression analyses were conducted to study the associations between class membership and depressive symptoms and suicidal ideation 2 years later. The moderating role of sex in the associations between latent classes and depressive symptoms was explored. Five latent classes were identified: Low Vulnerability (42%), Substance Use Only (13%), Moderate Vulnerability (28%), Conduct Problems (8%) and High Vulnerability (9%). Compared with the Low Vulnerability class, the probabilities of presenting depressive symptoms were higher for the Substance Use Only class, OR = 1.93, 95% CI [1.21, 3.06], the Moderate Vulnerability class, OR = 2.96, 95% CI [2.09, 4.20], the Conduct Problems class, OR = 3.03, 95% CI [1.84, 4.98], and the High Vulnerability class, OR = 5.4, 95% CI [3.42, 8.53]. Furthermore, interaction effects with sex were identified in relation to depressive symptoms only. The probability of presenting suicidal ideation was higher only for the High Vulnerability class, OR = 4.51, 95% CI [2.41, 8.43]. This study highlights the importance of a person-centered perspective that considers both vulnerability subtypes and sex because these associations are complex rather than linear or additive.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".