Understanding as a foundation for optimising treatment outcomes in six Aboriginal community-controlled alcohol and other drug residential rehabilitation services in New South Wales Australia
Bibliographic record
Abstract
Indigenous peoples in similarly colonised countries (Australia, New Zealand, Canada, United States of America) experience poorer health outcomes compared to their non-Indigenous counterparts. Substance use disorders are directly linked to poorer health and social and emotional wellbeing. Consequently, one response has been the development of Indigenous treatment services for substance use disorders, of which alcohol and other drug (AoD) residential rehabilitation is a key component. This thesis examines what is known about clients on admission to these services in New South Wales (NSW), Australia, and considers the relationship between clients’ intake data and their outcomes. Specifically, a PRISMA compliant systematic review was undertaken (Chapter 2) to collate the current level of published evidence on Indigenous AoD residential rehabilitation services. Chapter 3 analysed client admissions data from all six Indigenous AoD residential rehabilitation services in NSW to identify similarities and variations across services in relation to key program features (e.g., eligibility criteria, primary substance of concern, length of stay). Chapter 4, conducted across the same six services, examined admissions data and treatment outcomes to predict which clients: 1) left treatment early (≤ 28 days), 2) self-discharged or house discharged, and 3) were re-admitted to the same service within two years. The predictors were Aboriginal status, age, justice system referral and primary substance of concern. To our knowledge this is the first-time, in Australia and internationally, that data have been combined from multiple Indigenous AoD residential rehabilitation services into the same analyses and results published in the peer-reviewed literature. This thesis highlights an opportunity for policy makers and services to increase the focus of treatment towards an outcomes-based approach. However, to do so, there is a need to increase standardisation of data collection processes and the use of data in real-time, and to increase stakeholder involvement in delivering Indigenous AoD residential rehabilitation services.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".