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Record W4406076413 · doi:10.1080/13218719.2024.2416637

Supporting people with an acquired brain injury (ABI) involved in the criminal justice system: justice and allied health professionals’ insights

2025· article· en· W4406076413 on OpenAlexaff
Bernadette J. Saunders, Gaye Lansdell, Anna Eriksson

Bibliographic record

VenuePsychiatry Psychology and Law · 2025
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Victoria
FundersAustralian Institute of Criminology
KeywordsCriminal justiceEconomic JusticeCriminologyPsychologyHealth professionalsProject commissioningPolitical sciencePublishingHealth careLaw

Abstract

fetched live from OpenAlex

Concerning rates of acquired brain injury (ABI) among adults and young people involved in the criminal justice system raise questions about support needs and issues that they face within, and at the juncture between, community-based service systems and criminal justice systems. Data from two studies in Victoria Australia, between 2015 and 2021, highlight key issues that experienced professionals identify as contributors to the repeated cycling through the criminal justice system of people living with an ABI. Systemic issues including poor ABI-literacy; failure to identify, assess or diagnose an ABI (especially among young people); insufficient specialist ABI services; inadequate care coordination and holistic, ongoing community support; and poor integration and information-sharing between custodial and community-based services lead to inappropriate and indifferent responses and attitudes. Our findings, discussion and recommendations provide insights to assist justice and allied health professionals, and to policymakers to thwart patterns of entrenchment in the criminal 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 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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.025
GPT teacher head0.373
Teacher spread0.348 · 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 designTheoretical or conceptual
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

Citations1
Published2025
Admission routes1
Has abstractyes

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