Patient Characteristics and Positive Outcomes Associated With a Novel Youth Inpatient Program for Concurrent Disorders
Bibliographic record
Abstract
ABSTRACT: Concurrent substance use and mental health disorders in youth are a major public health concern and require specialized and comprehensive services. In this paper, a novel inpatient tertiary care facility serving youth aged 13 to 18 with significant concurrent substance use and mental health issues is introduced. The development of this unit was prompted by the opioid overdose crisis in British Columbia and serves as the third concurrent disorders unit in Canada catered specifically to an adolescent population. From its opening in 2017, preadmission and postadmission data from each patient was gathered with the aim of providing a robust image of the serviced patient population as well as the efficacy of this service model. Patients admitted to this program had significantly higher quality of life ( d = 0.65) and significantly lower suicidality ( d = 0.86) at discharge, compared with at admission. Patients identifying as female had significantly lower quality of life, higher suicidality, and higher prevalence of adverse childhood events compared with patients identifying as male. Results from this program evaluation outline the efficacy of a novel concurrent disorders program for youth while further providing an overview of clinical and relevant demographic characteristics from an underanalyzed patient population.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".