The temporal sequence of depressive symptoms, peer victimization, and self-esteem across adolescence: Evidence for an integrated self-perception driven model
Bibliographic record
Abstract
Depression is associated with a multiplicity of adverse outcomes in adolescence, including peer victimization and low self-esteem. Depressive symptoms, peer victimization, and self-esteem are linked in cross-sectional studies, but no longitudinal study has been conducted assessing their developmental pathways in one integrated model across adolescence. We explored their temporal sequencing in a normative sample of 612 Canadian adolescents (54% girls) assessed annually over 5 years (Grade 7 to Grade 11). Potential confounders such as biological sex, ethnicity/race, and parent income and education were statistically controlled. We found evidence for the vulnerability model (self-esteem predicting depression) and the symptoms-driven model (depression predicting peer victimization). Our findings also supported the integration of these pathways into a self-perception driven model characterized by the indirect effect of self-esteem on later peer victimization via depressive symptoms. Specifically, poor self-esteem initiated a developmental cascade that led to poor mood and poor peer relations. These results highlight the importance of helping youth form a healthy identity that promotes positive mental health and peer relations, and the need to intervene with depressed, victimized, and at-risk adolescents to instill positive self-regard. Our results also emphasize the central role that self-perceptions play in the onset and maintenance of poor outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".