Examining the Risk and Predictive Factors for Substance Use and Mental Health among Adolescent Youth in Out-of-Home Care
Bibliographic record
Abstract
The family is a core social institution that performs a number of critical functions, the most enduring and important of which is the responsibility for socializing children. The role of the family, parenting behaviours, and parent-child relations continue to be a focal point for explaining a number of cognitive and behavioural outcomes in children and youth. More recently, the relationship between growing up in out-of-home care, health and well-being, and substance use has been garnering increasing attention. The key purpose of this dissertation is to contribute to the existing body of literature by examining the impact of a number of focal and control variables on substance use and mental health among youth in out-of-home care. Although prior research has identified these variables as important factors contributing to substance use and poor mental health among this sample of youth, no study has investigated the direct and indirect effects of each factor, while controlling for the other effects. The current dissertation, divided into three research papers, uses a sample of 1419 youths aged 16-17 from the 2016 Ontario Looking After Children (OnLAC) project data to investigate the risk and predictive factors of substance use and mental health among a sample of youth preparing to emancipate from care. The policy implications associated with these findings, include the strengthening of initial placement decisions, stronger emphasis on the development of strong bonds, reducing the amount of unsupervised living placements, and allocating funding to interventions that target specific problematic behavioural characteristics before these youth reach adolescence. These implications are relevant to service providers and child welfare professionals as programs aimed towards youth successfully transitioning out of care remains a priority.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".