Supporting alcohol brief interventions and pharmacotherapy provision in Australian First Nations primary care: exploratory analysis of a cluster randomised trial
Bibliographic record
Abstract
INTRODUCTION: Primary care provides an important opportunity to detect unhealthy alcohol use and offer assistance but many barriers to this exist. In an Australian context, Aboriginal Community Controlled Health Services (ACCHS) are community-led and run health services, which provide holistic primary care to Aboriginal and Torres Strait Islander peoples. A recent cluster randomised trial conducted with ACCHS provided a service support model which showed a small but significant difference in provision of 'any treatment' for unhealthy alcohol use. However, it was not clear which treatment modalities were increased. AIMS: To test the effect of an ACCHS support model for alcohol on: (i) delivery of verbal alcohol intervention (alcohol advice or counselling); (ii) prescription of relapse prevention pharmacotherapies. METHODS: Intervention: 24-month, multi-faceted service support model. DESIGN: cluster randomised trial; equal allocation to early-support ('treatment') and waitlist control arms. PARTICIPANTS: 22 ACCHS. ANALYSIS: Multilevel logistic regression to compare odds of a client receiving treatment in any two-month period as routinely recorded on practice software. RESULTS: Support was associated with a significant increase in the odds of verbal alcohol intervention being recorded (OR = 7.60, [95% CI = 5.54, 10.42], p < 0.001) from a low baseline. The odds of pharmacotherapies being prescribed (OR = 1.61, [95% CI = 0.92, 2.80], p = 0.1) did not increase significantly. There was high heterogeneity in service outcomes. CONCLUSIONS: While a statistically significant increase in verbal alcohol intervention rates was achieved, this was not clinically significant because of the low baseline. Our data likely underestimates rates of treatment provision due to barriers documenting verbal interventions in practice software, and because different software may be used by drug and alcohol teams. The support made little impact on pharmacotherapy prescription. Changes at multiple organisational levels, including within clinical guidelines for primary care, may be needed to meaningfully improve provision of alcohol treatment in ACCHS. TRIAL REGISTRATION: ACTRN12618001892202 (retrospectively registered on 21/11/2018).
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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 | 0.037 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".