Working both-ways: the role of culture and relapse prevention medicines in care for unhealthy alcohol use in Aboriginal and Torres Strait Islander Community Controlled Health Services
Bibliographic record
Abstract
Background Globally, unhealthy alcohol use disrupts the wellbeing of communities and is a leading cause of premature death and disability. Primary care services help individuals with the full spectrum of unhealthy alcohol use. Incorporating First Nations approaches alongside standard evidence-based (Western) approaches, may improve intervention acceptability and effectiveness. Aims To identify peer-reviewed studies of outpatient alcohol treatments for First Nations peoples of Australia, New Zealand, USA, and Canada; explore the role of culture in alcohol care delivered in 11 Aboriginal Community Controlled Health Services (ACCHSs); and assess prescription rates of alcohol relapse prevention medicines in 22 ACCHSs over 12-months and identify associated factors. Methods A systematic review summarises scope and nature of literature including culture underpinning interventions (Western; First Nations culture; bicultural). A qualitative analysis of interview data with First Nations ACCHS staff investigates the role of culture in alcohol care using a novel analytic method. Routinely collected data on prescription rates of relapse prevention medicines were analysed, and (using logistic regression) associated client or service factors. Results and discussion Cultural and bicultural interventions were described for First Nations peoples in New Zealand, USA and Canada, but not Australia. US-based studies included trials of relapse prevention medicines. The qualitative study highlighted a comprehensive bicultural model of alcohol care. Prescription rates were low across the 22 ACCHSs. Prescription was more likely for clients screened with AUDIT-C, who were male, middle age, and attended an urban service. Conclusion Internationally, First Nations culture and healing traditions have played a role in primary care treatment for unhealthy alcohol use alongside Western approaches. This research describes use of a bicultural approach in Australian ACCHSs.
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 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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".