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Record W4250421359 · doi:10.24124/2020/59071

Assessment and treatment to support youth involved in the criminal justice system: a practicum report on youth forensic psychiatric services

2020· dissertation· en· W4250421359 on OpenAlexaff
Emma van Vliet

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsWestern University
Fundersnot available
KeywordsPracticumCriminal justiceEconomic JusticeSocial workCriminologyPsychologyMedical educationPsychiatryPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

This practicum report explores a social work role at the Ministry of Children and Family Development’s Youth Forensic Psychiatric Services (YFPS), located at the North Region Outpatient Clinic in Prince George, British Columbia. YFPS receives referrals through court orders and probation officers to offer comprehensive assessment and treatment services to youth who are involved with the youth criminal justice system. Youth may become involved with the justice system for various reasons and, once involved with the justice system, have multiple options for treatment and/ or rehabilitation. This report explores some of the resources youth may become involved with and how social workers through YFPS play a role in supporting youth in the justice system. The main goal of my graduate practicum was to broaden my social work skills by exposing myself to a new social work field and client population. Within this larger goal, my learning objectives focused on increasing knowledge and skills in conducting and writing assessments and broadening my clinical knowledge, as well as focusing on how my work at YFPS fit into Trauma-Informed and Anti-Oppressive lenses. Overall, I was able to develop new skills and work collaboratively to offer comprehensive services to youth who were involved in the justice system.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.002
Scholarly communication0.0030.002
Open science0.0020.007
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0040.002

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 head0.063
GPT teacher head0.363
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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