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Record W2892459106 · doi:10.1177/0093854818799379

Improving Case Plans and Interventions for Adolescents on Probation: The Implementation of the SAVRY and a Structured Case Planning Form

2018· article· en· W2892459106 on OpenAlexafffund
Jodi L. Viljoen, Catherine S. Shaffer, Nicole M. Muir, Dana M. Cochrane, Etta Brodersen

Bibliographic record

VenueCriminal Justice and Behavior · 2018
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsNova Scotia Health AuthoritySimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychological interventionPlan (archaeology)PsychologyHuman factors and ergonomicsPoison controlSuicide preventionSample (material)Injury preventionApplied psychologyMedicineRisk analysis (engineering)Process managementPsychiatryMedical emergencyEngineering

Abstract

fetched live from OpenAlex

Even when probation officers use risk assessment tools, many of their clients’ needs remain unaddressed. As such, we examined whether the implementation of the Structured Assessment of Violence Risk in Youth (SAVRY) and a structured case planning form resulted in better case plans as compared with prior practices (i.e., a nonvalidated local tool and an unstructured plan). Our sample comprised 216 adolescents on probation who were matched via propensity scores. Adolescents in the SAVRY/Structured Plan condition had significantly better case plans than those in the preimplementation condition. Specifically, following implementation, adolescents’ high need domains were more likely to be targeted in plans. Plans also scored higher on other quality indicators (e.g., level of detail). These improvements appeared to be due primarily to the structured plan rather than the SAVRY. Overall, our findings highlight that, just as structure can improve risk assessments, so too might structure improve case plans.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.377

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 head0.085
GPT teacher head0.417
Teacher spread0.331 · 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 designOther design
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

Citations19
Published2018
Admission routes2
Has abstractyes

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