Storm and stress:an investigation of adolescents'use of behavioral and cognitive emotion regulation strategies and their engagement in risky behaviours
Bibliographic record
Abstract
The current study investigates the relationship between adolescents' use of adaptive and maladaptive behavioral (i.e., talking to a friend or bullying others) and cognitive (i.e., planning what can be done better the next time, or dwelling on one's thoughts and feelings) emotion regulation strategies following the experience of a negative event and their engagement in risky behaviors. Seventy-eight adolescents from six Montreal inner-city high schools completed the Regulation of Emotions Questionnaire (REQ-2; Phillips & Power, 2007), the Cognitive Regulation of Emotions Questionnaire (CERQ; Garnefski, Kraaij, & Spinhoven, 2002), and the Risky Behaviors Questionnaire for Adolescents (RBQ-A; Auerbach, & Abela, 2008). Results indicate that although adolescents are more likely to use behavioral than cognitive emotion regulation strategies in response to a negative event, the use of adaptive cognitive strategies is associated with a lower incidence of engagement in risky behaviors; whereas, the use of maladaptive behavioral and cognitive strategies are related to an increase in adolescents' risky behaviors. However, contrary to our hypothesis, adolescents' use of adaptive behavioral strategies is not related to adolescents' engagement in risky behaviors.
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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 | 0.001 | 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".