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Record W2971857585 · doi:10.1177/0093854819873019

Bridging Risk Assessments to Case Planning: Development and Evaluation of an Intervention-Planning Tool for Adolescents on Probation

2019· article· en· W2971857585 on OpenAlexafffund
Jodi L. Viljoen, Dana M. Cochrane, Catherine S. Shaffer, Nicole M. Muir, Etta Brodersen, Billie Joe Rogers, Kevin S. Douglas, Ronald Roesch, Robert J. McMahon, Gina M. Vincent

Bibliographic record

VenueCriminal Justice and Behavior · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsNova Scotia Health AuthoritySimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychological interventionIntervention (counseling)VignettePoison controlRisk assessmentSuicide preventionHuman factors and ergonomicsApplied psychologyPsychologyInjury preventionRisk managementPlan (archaeology)Medical educationMedicineMedical emergencyComputer scienceSocial psychologyComputer securityPsychiatryBusiness

Abstract

fetched live from OpenAlex

Even though risk assessment tools are often intended to inform case planning, they do not provide much direct guidance. As such, we developed an intervention-planning tool called the Adolescent Risk Reduction and Resilient Outcomes Work-Plan (ARROW) to accompany the Structured Assessment of Violence Risk in Youth. The ARROW includes a decision support system, guide, and training, and is one of the first tools of its kind. To evaluate the ARROW, we conducted two studies: (a) a vignette study with 178 professionals and (b) a field study with 320 propensity-score matched adolescents. Most professionals (>98%) rated the ARROW as useful. Moreover, compared with (a) unstructured plans and (b) a simple form, ARROW plans were more likely to include supported interventions, adhere to best practices, and integrate culturally tailored approaches for Indigenous adolescents. Formulations also showed improvements. However, further research is needed on strategies to bridge risk assessment and risk management.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.591
GPT teacher head0.672
Teacher spread0.081 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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