HOSPITAL REFERRAL TO A COMMUNITY PROGRAMME FOR YOUTH INJURED BY VIOLENCE: A FEASIBILITY STUDY
Bibliographic record
Abstract
Background Youth violence is an immense burden in Canada. Violence is a recurring condition—20–40% of youth injured by violence will be reinjured within the next year. Aims/Objectives/Purpose To assess the feasibility of referring youth injured by violence to community based intervention programmes. Methods Youth presenting to St. Michael's Hospital Emergency Department and Trauma Service were approached to participate in the study. Information about the study was conveyed using a computer tablet. Youth completed an online baseline survey. If a youth consented to participation, a research coordinator linked the youth with their chosen community partner. Results/Outcome Sixty youth (27% of eligible patients) were approached and 19 (32%) chose to participate. 92% were male and the average age was 19.3. In the prior 6 months, 71% of participants had been in a physical fight, with 35% of all participants having visited an emergency department for a fight related injury. Fourteen youth (70% of enrolled) chose a community programme; however, five were unable to be contacted the following day to facilitate the referral. Significance/Contribution to the Field This feasibility study demonstrates the complexity of recruiting and referring a high-risk population to community resources. The results from this study will be used to develop a larger study to determine the effectiveness of these referrals in reducing future intentional injury.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".