Rate of symptoms of dual diagnosis in the child welfare system in Canada : profile of adolescents and their caregiver in the CIS-2003
Bibliographic record
Abstract
Research in the field of dual diagnosis (the coexistence of symptoms indicative of a substance abuse problem and a mental health problem) has expanded immensely over the past 15 years. Unfortunately, much of the existing literature available on this topic is limited to adult populations. The researcher explored the rate of dual diagnosis in the adolescent population by conducting a secondary data analysis of the Canadian Incidence Study of Child Abuse and Neglect (CIS-2003; Trocme et al., 2005). The rate of having one or more substance abuse problems in the CIS-2003 was 8.8% and the rate of having one or more mental health problems was 23.6%. Dual diagnosis was found to be under-reported in the child welfare system in Canada. Results of the secondary data analysis indicate that 4.4% of the total sample of adolescents aged between 10 and 15 years old had symptoms indicative of a dual diagnosis over the 3 month study period (n=4381). By providing a profile of child and caregiver characteristics and risk factors associated with dual diagnosis, clinicians from all realms can become better equipped to understand these issues. In the future, Social Workers and other professionals can work collaboratively on identification of DD and improving outcomes for youth and families affected by symptoms of a dual diagnosis.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".