Health Promotion Strategies to Address Gambling-Related Harm in Indigenous Communities: A Review of Reviews
Bibliographic record
Abstract
The evolution of commercial gambling and its expansion into digital arenas has increased opportunities for people all over the world—including Indigenous people—to gamble. While there is considerable evidence for the suitability of a health promotion approach to improving the health and well-being of Indigenous communities worldwide, the evidence-base does not extend to the field of gambling research. A systematic review of reviews was conducted to identify relevant reviews in crossover areas of interest: interventions to address gambling-related harm in Indigenous populations and/or health promotion interventions on related health or behavioural outcomes. The quality of reviews was critically assessed—13 fit the inclusion criteria. Principal themes were characterised as being either related to ‘cultural,’ ‘structural,’ or ‘methodological’ factors. Findings indicate that an appropriate model of health promotion to address Indigenous gambling would necessarily involve careful consideration of all three elements. Applying a health promotion approach to the context of Indigenous gambling harms is increasingly relevant considering recent conceptual shifts in key areas, but there is currently limited evidence to guide the implementation and evaluation of such strategies. This review highlights what published evidence is available to strengthen future research in this area.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".