Strong & Solid Spirit: design & development of a treatment programme for First Nations men incarcerated for sexual offences
Bibliographic record
Abstract
Little research has examined effective treatment for First Nations Peoples who have committed sexual offences, although available evidence suggests poorer retention rates and treatment outcomes for this population. Thus, calls have been made to improve treatment programming, including for the development of treatment programmes designed specifically for First Nations men. Documented in this paper is a three-phase process undertaken to design, develop, and refine a new treatment programme in Queensland Australia, for First Nations men who have committed sexual offences. This incorporated: (i) a review of relevant literature; (ii) participatory interview research with First Nations men, community members, and professionals; and (iii) co-design. The new programme, Strong & Solid Spirit: Community & Accountability (SSS) is introduced. Key transferable lessons for research and practice in other jurisdictions are offered in light of this work.PRACTICE IMPACT STATEMENT This study introduces the Strong & Solid Spirit: Community & Accountability (SSS) programme, a culturally tailored treatment programme for Australian First Nations Men who have committed sexual offences. Key transferable practice lessons highlighted by this work include (1) valuing First Nations and user voices, and co-design processes; (2) centring culture in programme design and implementation and (3) building a First Nations workforce.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".