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Record W4388021750 · doi:10.1177/00938548231207059

Engaging First Nations Australians in Correctional Treatment: The Perspectives of Program Recipients and Facilitators

2023· article· en· W4388021750 on OpenAlexaboutno aff
M Trudgett, Andrew McGrath, Bianca Spaccavento

Bibliographic record

VenueCriminal Justice and Behavior · 2023
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsFacilitatorRehabilitationNursingPerceptionMedicineMedical educationPublic relationsPsychologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Developing and delivering effective rehabilitation programs that meet the specific needs of First Nations people and overcome barriers to engagement has been suggested as a way to address the overrepresentation of First Nations Australians in the correctional system. This project used a critical realist epistemology to understand perceptions of First Nations people participating in rehabilitation programs to contribute to improvements in treatment responsivity. Semi-structured interviews were conducted with five First Nations people serving community-based orders and five First Nations Program Facilitators. The data were analyzed thematically. Four overarching themes emerged: (a) the importance of culture and colonization, (b) intrinsic motivation to change, (c) communication and language: the role of the First Nations facilitator, and (d) connection: life after jail. These findings highlight the need for cultural healing as a crucial factor for programs aimed at First Nations Australians.

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)0.015
metaresearch head score (Gemma)0.020
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 score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0200.009
Scholarly communication0.0070.004
Open science0.0020.008
Research integrity0.0030.008
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 head0.173
GPT teacher head0.478
Teacher spread0.305 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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