A bibliometric review of drug and alcohol research focused on Indigenous peoples of Australia, New Zealand, Canada and the United States
Bibliographic record
Abstract
ISSUES: Indigenous peoples of Australia, New Zealand, Canada and the United States experience a disproportionately high burden of harms from substance misuse. Research is therefore required to improve our understanding of substance use in Indigenous populations and provide evidence on strategies effective for reducing harmful use. APPROACH: A search of 13 electronic databases for peer-reviewed articles published between 1993 and 2014 focusing on substance use and Indigenous peoples of Australia, New Zealand, Canada and the United States. Relevant abstracts were classified as data or non-data based research. Data-based studies were further classified as measurement, descriptive or intervention and their trends examined by country and drug type. Intervention studies were classified by type and their evaluation designs classified using the Cochrane Effective Practice and Organisation of Care (EPOC) data collection checklist. KEY FINDINGS: There was a statistically significant increase from 1993 to 2014 in the percentage of total publications that were data-based (P < 0.001). Overall, data-based publications were mostly descriptive for all countries (84-93%) and drug types (74-95%). There were fewer measurement (0-4%) and intervention (0-14%) publications for all countries and the percentage of these did not change significantly over time. Forty-two percent of intervention studies employed an EPOC evaluation design. IMPLICATIONS: Strategies to increase the frequency and quality of measurement and intervention research in the Indigenous drug and alcohol field are required. CONCLUSION: The dominance of descriptive research in the Indigenous drug and alcohol field is less than optimal for generating evidence to inform Indigenous drug and alcohol policy and programs. [Clifford A, Shakeshaft A. A bibliometric review of drug and alcohol research focused on Indigenous peoples of Australia, New Zealand, Canada and the United States. Drug Alcohol Rev 2017;36:509-522].
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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 | 0.023 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.164 | 0.223 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".