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Record W4387477322 · doi:10.1192/bja.2023.13

Violent behaviour in adolescents: assessment and formulation using a structured risk assessment tool

2023· article· en· W4387477322 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBJPsych Advances · 2023
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsRisk assessmentNeglectRisk managementPsychological interventionAgency (philosophy)PsychologyRisk management toolsPsychiatryMedicineApplied psychologyComputer securityBusinessSociologyComputer science

Abstract

fetched live from OpenAlex

SUMMARY Teenagers often present in crisis with risk issues, mainly risk to self but sometimes risk to others. Adolescent violence is commonplace and is not just the remit of adolescent forensic psychiatry. Clinicians may lack confidence assessing risk of violence and can neglect vital areas that are essential to reduce risk. Use of structured violence risk assessments enables the multi-agency professional network to formulate a young person's presentation and their violence in a holistic way and consequently develop targeted risk management plans addressing areas such as supervision, interventions and case management to reduce the risk of future violence. Of the several validated tools developed for young people, the Structured Assessment of Violence Risk – Youth (SAVRY™) is that most used by UK-based forensic adolescent clinicians. This article outlines the epidemiology, causes and purposes of violence among adolescents; discusses types of risk assessment tool; explores and deconstructs the SAVRY; and presents a fictitious risk formulation.

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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.090
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.025
GPT teacher head0.395
Teacher spread0.370 · 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