Temporal Relationships Among Depressive Symptoms, Risky Behavior Engagement, Perceived Control, and Gender in a Sample of Adolescents
Bibliographic record
Abstract
The goals of the study were to examine whether (a) risky behaviors precede depressive symptoms or, conversely, depressive symptoms predict risky behavior engagement; (b) gender moderates the relationship between risky behavior engagement and depressive symptoms; and (c) perceived control strengthens the association between risky behavior engagement and depressive symptoms. At Time 1, 118 adolescents completed self-report measures assessing perceived control, risky behavior engagement, and depressive symptoms. Follow-up assessments occurred every 6 weeks (Times 2–5), and participants completed measures assessing risky behavior engagement and depressive symptoms. Results indicated that neither risky behavior engagement nor depressive symptoms emerged as main effects for the sample as a whole. When examining the relationship between depressive symptoms and risky behavior engagement as a function of gender, boys', but not girls', risky behavior engagement predicted higher levels of depressive symptoms. Irrespective of whether we examined boys or girls, depressive symptoms did not predict risky behavior engagement. With regards to the role of cognitive vulnerability, adolescents with lower levels of perceived control reported higher levels of depressive symptoms following risky behavior engagement. These findings suggest that both gender and cognitive vulnerability factors may potentiate the relationship between risky behavior engagement and subsequent depressive symptoms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".