Models of substance use in adolescents with and without psychosis.
Bibliographic record
Abstract
INTRODUCTION: The use of substances is a major concern with adolescents with psychotic disorders, as it can have detrimental effects on psychotic symptoms and other aspects of functioning. The purpose of this study is to increase our understanding of the association between substance use and psychosis in adolescents by testing three models of substance use: the normative development model, the deviance-prone model, and the affect regulation model. METHODS: Participants were 35 adolescents with a psychotic disorder, and 35 typically developing adolescents. Measures used: Personal Experience Screening Questionnaire, Youth Self-Report, Beck Depression Inventory, and the Beck Anxiety Inventory. RESULTS: The normative development model, hypothesizing that mild substance use leads to better socio-emotional adjustment, was not supported for either group. The deviance-prone model was supported for both groups, indicating that rule-breaking behaviour and aggression significantly predicted substance use. The affect-regulation model was supported for adolescents with psychosis only, indicating that negative affect significantly predicted substance use. CONCLUSIONS: The treatment of substance misuse in adolescents with psychosis may be complicated by a number of factors including deviant behaviour and negative affective symptoms. Thus, the current results point to the importance of integrated treatments to help reduce substance misuse and associated problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".