What can primary care services do to help First Nations people with unhealthy alcohol use? A systematic review: Australia, New Zealand, USA and Canada
Bibliographic record
Abstract
Background: First Nations peoples of Australia, New Zealand, the United States of America (USA) and Canada are more likely to be non-drinkers than other people in these countries. However, those who do drink may be at greater risk of alcohol-related harms (at a population level) due to the ongoing impacts from colonisation and associated oppression. Addressing unhealthy drinking (drinking above recommended limits including alcohol use disorders) in primary care settings is one important way to increase accessibility of treatment. Methods: This systematic review identifies peer-reviewed studies of alcohol treatments delivered in primary care or other non-residential settings for First Nations peoples of Australia, New Zealand, USA and Canada. Literature searches were conducted in seven academic databases from their inception until March, 2020. We assessed evidence of treatment or implementation effectiveness, perceived acceptability or accessibility, and the study quality as assessed by the AXIS tool and by a measure of community participation in the research process. Results: Twenty-eight studies were included, published between 1968 and 2018. Studies reported on a range of alcohol treatments, from brief intervention to ambulatory withdrawal management, relapse prevention medicines, and cultural therapies. Brief intervention was the most studied approach. Cultural healing practices and bicultural approaches were a key theme amongst several studies. Four studies measured treatment effectiveness, including one randomised controlled trial (naltrexone vs naltrexone plus sertraline vs placebo) and two uncontrolled trials of disulfiram. Of the six implementation studies, three were (hybrid) effectiveness-implementation designs. Most of the remaining studies (n = 21) focused on treatment accessibility or acceptability. Community participation in the research process was poorly reported in most studies. Conclusions: Research evidence on how best to care for First Nations peoples with unhealthy alcohol use is limited. Trials of naltrexone and disulfiram presented promising results. Cultural and bicultural care were perceived as highly important to clinical staff and clients in several studies. More effectiveness studies on the full scope of alcohol treatments are needed. Greater community participation in research and more transparent reporting of this in study methods will be key to producing quality research that combines scientific rigour with cultural appropriateness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".