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HOSPITAL REFERRAL TO A COMMUNITY PROGRAMME FOR YOUTH INJURED BY VIOLENCE: A FEASIBILITY STUDY

2012· article· en· W2094399498 on OpenAlexaffabout
Carolyn Snider, Avery B. Nathens

Bibliographic record

VenueInjury Prevention · 2012
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsReferralMedicineSuicide preventionPoison controlEmergency departmentIntervention (counseling)PopulationOccupational safety and healthInjury preventionMedical emergencyHuman factors and ergonomicsFamily medicineNursingEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.103
GPT teacher head0.419
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes2
Has abstractyes

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