Effect of a wraparound care emergency department intervention on substance misuse among youth injured by violence
Bibliographic record
Abstract
There is a growing concern surrounding youth injured by violence. Violent injuries are the fourth most common cause of death and the leading reason for a youth to visit an emergency department (ED) in Canada. Youth who have sustained their first violent injury have been shown by numerous studies to have a higher risk of trauma recidivism and are at risk to return to the hospital for new assault injuries. Alcohol and drug use has been suggested as a principal risk factor for victimization and trauma recidivism. Assault-injured youth seeking ED care show higher levels of substance use than their comparison group, with 20.8% of these youth reporting substance use before the injury and higher odds for substance misuse in the past 6 months. For this study, a chart review was conducted on youth aged 14-24 years enrolled in a randomized control trial for the Emergency Department Violence Intervention Program (EDVIP) in Winnipeg, Manitoba. Enrollment required presenting with an injury caused by interpersonal violence. The program delivered wraparound care to the injured youth, providing individualized care management based on their needs and risk factors. Information from patient charts was gathered on substance use and compared to the control group of youth who did not receive the intervention. The purpose of the study was to identify if visits related to substance use decreased with the intervention. It is our hypothesis that the wraparound care model will decrease the number of visits to the ED related to substance use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".