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Record W6908587904 · doi:10.25958/4te4-5640

The first international study into the characteristics and statistical effectiveness of treatment for Indigenous and First Nations People who have sexually offended: A meta-analysis and survey of routine practice

2025· dissertation· en· W6908587904 on OpenAlexaboutno aff

Bibliographic record

VenueAustralasian Journal of Paramedicine · 2025
Typedissertation
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousRecidivismCulturally appropriateOddsDeveloping countryNarrativeReproductive healthTraditional knowledge

Abstract

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Psycho-criminological literature has continued to examine post-treatment recidivism outcomes homogeneously for people who have sexually offended. Because results indicate clear differences between Indigenous and non-Indigenous people who have sexually offended, this project set out to understand the characteristics and effectiveness of treatment for Indigenous and First Nations people from four jurisdictions: Australia, New Zealand, Canada and the United States. A strategic three study strategy was implemented and resulted in a systematic narrative synthesis, meta-analysis and survey of sexual offender treatment organisations in Australia. The systematic review used a comprehensive and scientifically rigorous approach to identify studies (k = 14) that were critically appraised through an extensive data extraction process to report on demographic, treatment and recidivism data. Novel findings in relation to the characteristics of treatment for Indigenous people and the respective recidivism outcomes were provided. Over half of programs reported using a blended approach to treatment that included Western and culturally relevant practices, with the remaining studies taking a purely Westernised approach. The meta-analysis built on the systematic narrative review by statistically calculating the effectiveness of treatment for Indigenous and First Nations people from studies that contained a comparison group (k = 7). Using odds ratios and a random-effects model, findings suggest that although treatment was effective for Indigenous and First Nations people, it was most effective when programs utilised treatment that was of cultural relevance. Given there is limited published research on treatment outcomes for Indigenous and First Nations people, the final study overcame this barrier by directly surveying treatment providers in Australia (N = 12). This provided a foundation of knowledge and mapped out current practices in the field of treatment for Aboriginal and Torres Strait Islander people who had sexually offended and received treatment in Australia. Key insights into treatment practices were provided, including the development and theoretical foundations of treatment, current approaches, evaluation efforts and issues concerning the implementation of treatment for Aboriginal and Torres Strait Islander people. Although organisations implemented culturally relevant treatment approaches, it was more common to see Western therapeutic interventions at the forefront of practice. There was a lack of Aboriginal and Torres Strait Islander-specific risk and needs assessment tools, suggesting a lack of adherence to the principles of risk, need and responsivity among these organisations. Overall, results from all studies indicated that more research, stronger methodological approaches and clearer adherence to the principles of risk, need and responsivity were required before more definitive conclusions can be made. There are clear challenges faced by researchers and program facilitators to ensure programs are of a high calibre so that Indigenous and First Nations people achieve positive outcomes of reduced recidivism. Therefore, the thesis concludes with 21 recommendations to ensure the field continues to develop scholarly knowledge on best-practice approaches and methods of delivering treatment to Indigenous and First Nations people.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.043
GPT teacher head0.393
Teacher spread0.350 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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