Associations between developmental trajectories of peer victimization, hair cortisol, and depressive symptoms: a longitudinal study
Bibliographic record
Abstract
BACKGROUND: Peer victimization has been associated with long-lasting risks for mental health. Prior research suggests that stress-related systems underlying adaptation to changing environments may be at play. To date, inconsistent findings have been reported for the hypothalamic-pituitary-adrenal (HPA) axis, and its end product cortisol. This study tested whether peer victimization was associated with hair cortisol concentrations (HCC), and whether this association varied according to sex, timing, and changes in exposure. We also examined whether peer victimization differentially predicted depressive symptoms according to HCC. METHODS: The sample comprised 556 adolescents (42.0%; 231 males) who provided hair for cortisol measurement at 17 years of age. Peer victimization was reported at seven occasions between the ages of 6 and 15 years. RESULTS: Peer victimization was nonlinearly associated with HCC for boys only, whereas changes in peer victimization were related to HCC for boys and girls. Peer victimization predicted more depressive symptoms for all participants, except those with lower HCC. CONCLUSIONS: Our findings provide further support for persistent dysregulation of the HPA axis following exposure to chronic adversity, of which the expression may change according to sex and the severity of victimization.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".