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Record W2827118214 · doi:10.1177/0093854818784988

Predictive Validity of the MAYSI-2 and PAI-A for Suicide-Related Behavior and Nonsuicidal Self-Injury Among Adjudicated Adolescent Offenders on Probation

2018· article· en· W2827118214 on OpenAlexafffund
Catherine S. Shaffer, Erik Maurice Dante Gulbransen, Jodi L. Viljoen, Ronald Roesch, Kevin S. Douglas

Bibliographic record

VenueCriminal Justice and Behavior · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaMichael Smith Health Research BC
KeywordsPredictive validityPsychologySuicide preventionPoison controlClinical psychologyInjury preventionHuman factors and ergonomicsOccupational safety and healthPsychiatryIncremental validityTest validityMedicinePsychometricsMedical emergency

Abstract

fetched live from OpenAlex

This prospective study evaluated the ability of the MAYSI-2 and PAI-A to predict suicide-related behavior (SRB) and nonsuicidal self-injury (NSSI) among adjudicated adolescent offenders on probation. Predictive validity of the MAYSI-2 for SRB and NSSI has generally been postdictively examined among detained adolescents. In addition, no published studies have examined the predictive validity of the PAI-A for SRB and NSSI among adolescent offenders. Neither the MAYSI-2 nor PAI-A added incremental predictive validity above lifetime SRB or NSSI. However, several MAYSI-2 and PAI-A subscales were predictive of SRB or NSSI. With some exceptions, most recommended instrument cut-off scores differentiated between low-risk and high-risk youth. These findings suggest that the MAYSI-2 and PAI-A hold promise for evaluating SRB and NSSI among justice-involved youth. In addition, these findings contribute to more informed decisions regarding the use of these tools and can be used to inform SRB and NSSI prevention efforts.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.810

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 studies0.0000.001
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 head0.070
GPT teacher head0.342
Teacher spread0.272 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

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