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Record W4396664688 · doi:10.1080/13218719.2024.2330476

The living experience of First Nations Peoples and Forensic Mental Health systems: listening to the deep stories behind the numbers

2024· article· en· W4396664688 on OpenAlexaboutno aff
Elizabeth McEntyre, Georgia Lyons, Anina Johnson, Kimberlie Dean

Bibliographic record

VenuePsychiatry Psychology and Law · 2024
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMental Health Commission
KeywordsMental healthActive listeningForensic scienceCriminal justicePsychologyForensic psychiatryPsychiatryCriminologyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

While First Nations Peoples in Australia experience high rates of criminal justice contact, there is limited research on their experiences of the forensic mental health system. This study aims to develop new understandings of how First Nations Peoples experience and understand the forensic mental health system in NSW. Interviews were conducted with ten First Nations Peoples in contact with the forensic mental health system, including forensic patients and their family members. Participants described challenging life experiences prior to their contact with the forensic mental health system, with community services often failing to respond to their mental health needs. While participants reported some positive experiences with the forensic mental health system, they ultimately described an urgent need for culturally appropriate programs that facilitate connections to family and Community. Forensic mental health services should be co-designed alongside First Nations Peoples and communities to improve outcomes and avoid re-traumatisation through contact with services.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.968
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0170.019
Scholarly communication0.0080.009
Open science0.0010.012
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.321
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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