Adolescent Behavioral, Affective, and Cognitive Engagement in School: Relationship to Dropout
Bibliographic record
Abstract
BACKGROUND: High school dropout represents an important public health issue. This study assessed the 3 distinct dimensions of student engagement in high school and examined the relationships between the nature and course of such experiences and later dropout. METHODS: We administered questionnaires to 13,330 students (44.7% boys) from 69 high schools in the province of Quebec (Canada). During 3 consecutive high school years, students reported their behavioral, emotional, and cognitive engagement to school. Information on later dropout status was obtained through official records. RESULTS: Although many adolescents remained highly engaged in high school, one third reported changes, especially decreases in rule compliance, interest in school, and willingness to learn. Students reporting low engagement or important decrements in behavioral investment from the beginning of high school presented higher risks of later dropout. CONCLUSION: School-based interventions should address the multiple facets of high school experiences to help adolescents successfully complete their basic schooling. Creating a positive social-emotional learning environment promises better adolescent achievement and, in turn, will contribute to a healthier lifestyle.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".