Self-regulatory contributions to depressive symptoms in a community sample of youth
Bibliographic record
Abstract
Objective Depression is prevalent in youth, with concerning implications for their current and future function. Risk for depression is elevated pending how youth attempt to regulate their affective experiences (emotion regulation; ER) and the effectiveness with which they use higher-order cognitive abilities to work towards and attain their goals (executive functions; EF). According to the impaired disengagement hypothesis, depression may arise when ineffective EFs lead to reliance on maladaptive forms of ER—such asrumination. This study investigates rumination, other ER strategies, and EF challenges as correlated predictors of depressive symptoms in youth. Method: A community sample of 191 youth 11–18 years ( M = 13.47, SD = 1.48, 108 females, 48 % White, 5 % Black, 5 % Hispanic, 42 % Other) completed self-report measures of their mental health, ER strategies, and ability to apply EF abilities in their everyday lives. Results: Depression was elevated in youth who identified as female and who endorsed higher levels of EF-challenge, greater dispositional use of maladaptive ER, and lesser dispositional use of adaptive ER. The association of youths' EF challenges and depressive symptoms was attenuated and no longer significant in a multivariate model that considered the association between their depressive symptoms anduse of self-blaming rumination, catastrophizing, expressive suppression, and positive reappraisal. Conclusions: Early identification of self-regulatory factors that increase youths' risk of depression, including the experience of everyday EF challenges and reliance on maladaptive forms of ER, has potential to guide prevention and treatment efforts that mitigate the effects of depression in adolescence and adulthood.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".