Depressive symptoms and non‐suicidal self‐injury during adolescence: Latent patterns of short‐term stability and change
Bibliographic record
Abstract
INTRODUCTION: Depressive symptoms and non-suicidal self-injury not only increase in prevalence during adolescence, but they can also occur together. Both psychological problems seem to have similar precipitating conditions, suggesting they have transdiagnostic conditions-personal or contextual characteristics that contribute to co-occurrence. We sought to understand when these two problems co-occur and what is related to their co-occurrence. METHODS: = 13.65 years, SD = 0.64), 53.7% boys and 47.3% girls. Most of the adolescents were Swedish (89%), with parents who were married or cohabitating (68%). We also examined the transitions between profiles over time. RESULTS: Our results suggest that during this time frame, depressive symptoms and self-injury tend to emerge and stabilize or abate together. We also examined a broad array of predictors, including individual characteristics, emotion dysregulation, experiences with friends, parents' negative reactions to behavior, and school stress. The significant unique predictors suggest that adolescents who reported being subjected to relational aggression, having negative experiences while drinking, and low self-esteem had a greater probability of moving from moderate to high levels or maintaining high levels of depressive symptoms and self-injury, compared to adolescents classified in the other statuses. CONCLUSIONS: Focusing on negative interpersonal experiences and selfesteem as transdiagnostic conditions may guide research and aid clinicians in supporting adolescents who feel depressed and engage in self-injury.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".