Self-perceptions in relation to self-reported depressive symptoms in boys and girls.
Bibliographic record
Abstract
BACKGROUND: Low self-esteem is associated with depressive symptoms in children. However, the association between domains of self-esteem (e.g., self perceptions) and depressive symptoms may vary by gender. AIMS: This study evaluated self-perceptions in relation to self-reported depressive symptoms in boys and girls. METHODS: School children in grades 3 to 6 (n = 140; 54% boys; 46% girls) completed the Children's Depression Inventory (CDI) and The Self-Perception Profile for Children (SPPC) as part of a school-based intervention targeting anxious and depressive symptoms. The CDI was re-administered about 1 month later. Pearson correlations between the subscales of the SPPC and the average CDI T-scores were determined. Significant correlations were entered in stepwise regressions to predict depressive symptoms for the whole sample and then separately for boys and girls. RESULTS: Self-perceived scholastic competence, physical appearance, and behavioral conduct accounted for 19.8% of the variance in self-reported depressive symptoms overall. Behavioral conduct was a more salient predictor in boys (adjusted R(2) =0.146) whereas scholastic competence and physical appearance were more salient in girls (adjusted R(2) =0.203). CONCLUSION: Although replication is needed, boys and girls appear to have different self-perceptions in relation to depressive symptoms. Understanding these differences may help to inform clinical interventions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".