The relationship between emotion regulation and mental health in adolescents: Self-compassion as a moderator
Bibliographic record
Abstract
Background Emotion regulation and self-compassion play important roles in shaping the mental health and wellbeing. However, no studies to date have explored how these constructs may interact in the general adolescent population. This study examined the associations between different dimensions of self-compassion (compassionate self-responding and uncompassionate self-responding), emotion regulation (cognitive reappraisal and expression suppression) and mental health (depression and anxiety symptoms) among adolescents. It also examined whether self-compassion components moderate the relationships between emotion regulation and mental health symptoms. Method Data used in this study is drawn from a clustered RCT of a mental health prevention program conducted across nine Australian high schools. Multiple regression analyses were conducted to test whether self-compassion and emotion regulation strategies are significant predictors of anxiety and depression scores. Interactions between emotion regulation and self-compassion on anxiety and depression scores were also examined. Results 752 Australian adolescents were included in the study (M age =13.83, SD=0.78). Cognitive reappraisal and compassionate self-responding negatively predicted anxiety and depression scores, while expressive suppression and uncompassionate self-responding positively predicted these outcomes. Compassionate self-responding and uncompassionate self-responding differentially moderated the relationships between emotion regulation strategies and anxiety and depression scores. Conclusion The current study is the first to show the key role of self-compassion within adolescents’ emotion regulation framework. Future research should examine self-compassion and emotion regulation together as potential intervention targets for adolescents.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".