School dropout: The role of childhood conduct problems and depressive symptoms
Bibliographic record
Abstract
Abstract School dropout can be an ongoing process of academic failure and disengagement starting as early as elementary school. Given the importance of early identification of risk factors, the present study examines (a) whether early conduct problems and depressive symptoms predict a higher risk of school dropout, (b) whether depressive symptoms moderate the association between conduct problems and risk of school dropout, and (c) the sex differences in these associations. Using data from a longitudinal study on 744 children aged 6–9 (T1), a multiple linear regression was performed to test for the effect of conduct problems and depressive symptoms (T1) and the interaction between them on the risk of school dropout (T8), as well as for sex differences in these associations. Results showed that conduct problems significantly predicted a higher risk of school dropout 7 years later, while depressive symptoms did not. Depressive symptoms significantly moderated the effect of conduct problems on the risk of dropout, with conduct problems having a stronger effect in children with higher depressive symptoms. No sex differences were found. These results suggest that recognizing and treating depressive symptoms in children with conduct problems may be an important step in reducing their risk of dropout.
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 | 0.002 | 0.006 |
| 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 | 0.000 |
| 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".