Is low self-esteem a risk factor for depression? Findings from a longitudinal study of Mexican-origin youth.
Bibliographic record
Abstract
We examined the relation between low self-esteem and depression using longitudinal data from a sample of 674 Mexican-origin early adolescents who were assessed at age 10 and 12 years. Results supported the vulnerability model, which states that low self-esteem is a prospective risk factor for depression. Moreover, results suggested that the vulnerability effect of low self-esteem is driven, for the most part, by general evaluations of worth (i.e., global self-esteem), rather than by domain-specific evaluations of academic competence, physical appearance, and competence in peer relationships. The only domain-specific self-evaluation that showed a prospective effect on depression was honesty-trustworthiness. The vulnerability effect of low self-esteem held for male and female adolescents, for adolescents born in the United States versus Mexico, and across different levels of pubertal status. Finally, the vulnerability effect held when we controlled for several theoretically relevant 3rd variables (i.e., social support, maternal depression, stressful events, and relational victimization) and for interactive effects between self-esteem and the 3rd variables. The present study contributes to an emerging understanding of the link between self-esteem and depression and provides much needed data on the antecedents of depression in ethnic minority populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".