What is the prevalence of current alcohol dependence and how is it measured for Indigenous people in Australia, New Zealand, Canada and the United States of America? a systematic review
Bibliographic record
Abstract
© 2020 The Author(s). Background: Alcohol affects Indigenous communities globally that have been colonised. These effects are physical, psychological, financial and cultural. This systematic review aims to describe the prevalence of current (12-month) alcohol dependence in Indigenous Peoples in Australia, New Zealand, Canada and the United States of America, to identify how it is measured, and if tools have been validated in Indigenous communities. Such information can help inform estimates of likely treatment need. Methods: A systematic search of the literature was completed in six electronic databases for reports on current alcohol dependence (moderate to severe alcohol use disorder) published between 1 January 1989-9 July 2020. The following data were extracted: (1) the Indigenous population studied; country, (2) prevalence of dependence, (3) tools used to screen, assess or diagnose current dependence, (4) tools that have been validated in Indigenous populations to screen, assess or diagnose dependence, and (5) quality of the study, assessed using the Appraisal Tool for Cross-Sectional Studies. Results: A total of 11 studies met eligibility criteria. Eight were cross-sectional surveys, one cohort study, and two were validation studies. Nine studies reported on the prevalence of current (12-month) alcohol dependence, and the range varied widely (3.8-33.3% [all participants], 3-32.8% [males only], 1.3-7.6% [females only]). Eight different tools were used and none were Indigenous-specific. Two tools have been validated in Indigenous (Native American) populations. Conclusion: Few studies report on prevalence of current alcohol dependence in community or household samples of Indigenous populations in these four countries. Prevalence varies according to sampling method and site (for example, specific community versus national). Prior work has generally not used tools validated in Indigenous contexts. Collaborations with local Indigenous people may help in the development of culturally appropriate ways of measuring alcohol dependence, incorporating local customs and values. Tools used need to be validated in Indigenous communities, or Indigenous-specific tools developed, validated and used. Prevalence findings can inform health promotion and treatment needs, including funding for primary health care and specialist treatment services.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".