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Record W4386916858 · doi:10.1044/2023_persp-23-00012

Cognitive-Communication Disorders and Neurodisability in the Criminal Justice System: Emerging Roles, Responsibilities, and Opportunities for Speech-Language Pathologists

2023· article· en· W4386916858 on OpenAlexaff
Maya Albin, Catherine Wiseman‐Hakes

Bibliographic record

VenuePerspectives of the ASHA Special Interest Groups · 2023
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteMcMaster University
Fundersnot available
KeywordsRecidivismContext (archaeology)Criminal justiceIntervention (counseling)PsychologyCriminalizationEconomic JusticeCognitionPsychiatryCriminologyPolitical scienceBiology

Abstract

fetched live from OpenAlex

Purpose: The purpose of this clinical focus article is to provide a framework for speech-language pathologists (SLPs) supporting youth and adults in the criminal justice system (CJS) who have cognitive-communication disorders (CCDs) related to neurodisability. Method: We provide an overview of communication disorders and CCDs in the CJS as well as the underlying etiologies leading to neurodisability, with a primary focus on traumatic brain injury. Additionally, we provide a framework for understanding the underlying factors that contribute to criminalization and recidivism as well as elucidate the role of SLPs in the context of the CJS. Results: SLPs have an important role to play with youth and adults in the CJS and should be routinely involved in the screening of communication disorders; in the provision of assessments, intervention, education, and training; and in advocacy roles. Conclusion: The high prevalence of CCDs in the CJS affords a unique and important role for SLPs to be active members of the interdisciplinary justice team, which, ultimately, may improve outcomes and reduce the cycle of recidivism.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.123
GPT teacher head0.391
Teacher spread0.268 · 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 designQualitative
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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

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