Social Determinants of Health among Youth Seeking Substance Use and Mental Health Treatment.
Bibliographic record
Abstract
OBJECTIVE: The extent to which social determinants of health problems occur among youth with mental health and addiction concerns and the impact of social determinants on their treatment is unknown. This study examined the prevalence of social determinants of health problems among treatment-seeking youth, their perceptions of interference with treatment, and the association between social determinants of health and mental health/addiction difficulties. METHOD: Youth ages 15-24 seeking out-patient treatment for substance use concerns, with or without concurrent mental health concerns, reported on substance use, mental health and social determinants of health. Descriptive statistics and logistic regression analyses were used to determine the extent of social determinant of health problems and their relationship with mental health, substance use, and crime or violence problems. RESULTS: In all, 80% of youth endorsed social determinants of health concerns in at least one domain; nearly 70% identified financial concerns, and many identified substantial problems in each domain and anticipated treatment impacts. Youth most frequently identified financial problems as likely to impact treatment. Cumulative number of social determinants of health problems and individual domains of social determinants of health problems were related to overall mental health and addiction concerns. CONCLUSIONS: Given their prevalence and association with mental health and addiction concerns, social determinants of health problems should be routinely assessed among treatment-seeking youth and integrative services that address these concerns in addition to symptomatology should be considered.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".