Students adrift: An examination of school disengagement among clinically referred children and youth
Bibliographic record
Abstract
School disengagement is a significant problem experienced by many students and may be an indicator of or resultant from underlying difficulties. Indeed, the first onset of mental health difficulties typically occurs during childhood and adolescence. Utilizing large clinically referred samples of elementary and secondary school students at intake into community and inpatient mental health service agencies across the Province of Ontario, Canada, school disengagement was explored as associated with physical and mental health as well as peer and familial relationships, other and self-directed harm, and service intensity need. Age and sex differences were explored across studies to inform the development and implementation of targetted prevention and intervention programs for promoting school engagement and circumventing life-long consequences across the lifespan.\nFindings from the first paper indicated that physical and mental health distress were associated with school disengagement, while individual and relational strengths were associated with school engagement among clinically referred elementary and secondary students. Distinct predictive profiles of school disengagement were revealed for school-age children (ages 4 to 11 years) and youth (ages 12 to 18 years). Results from the second paper indicated that school disengagement was strongly associated with other- and self-directed harm among clinically referred elementary and secondary students. Notably, male youth were more likely to be at risk for harm to others, while female youth were more likely to be at risk for harm to self. Findings from the third paper indicated that students who are disengaged in school are two to four times more likely in odds to require high-intensity as compared to low-intensity services at intake into clinical services. Although service intensity need tended to decrease across development, this relationship was more stable for male students as compared to female students.\nTaken together, findings across the three papers highlight the necessity for early identification of student distress and provision of timely access to intervention. Further, the requirement of service integration across sectors working directly with students and their families is underlined. When school engagement problems are identified early, both immediate and long-term consequences, such as the manifestation of acute distress requiring crisis supports, can be prevented.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".