School Dropout and Conduct Disorder in Brazilian Elementary School Students
Bibliographic record
Abstract
OBJECTIVES: To evaluate the association between DSM-IV conduct disorder (CD) and school dropout in a sample of students from the third and fourth elementary grades at state schools in the capital of the southernmost state of Brazil. METHODS: In this case-control study, students that dropped out of schools (n = 44) and a control group who continued attending schools (n = 44) were assessed for CD and other prevalent mental disorders, using the Schedule for Affective Disorders and Schizophrenia for School Age Children, Epidemiological Version (K-SADS-E). RESULTS: The prevalence of DSM-IV CD was significantly higher in the school-dropout group than in control subjects (P < 0.001), both in the entire sample and in a subsample including only subjects under age 12 years (P = 0.001). Also, the odds ratio (OR) for school dropout was significantly higher in the presence of DSM-IV CD, even after controlling for potential confounding factors (age, estimated IQ, school repetition, family structure, and income) (P < 0.01). CONCLUSION: Our results extend to children and young adolescents previous findings from studies of older adolescents, suggesting an association between school dropout and CD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".