A mapping review of research on gambling harm in three regulatory environments
Bibliographic record
Abstract
BACKGROUND: Harmful gambling is a complex issue with diverse antecedents and resulting harms that have been studied from multiple disciplinary perspectives. Although previous bibliometric reviews of gambling studies have found a dominance of judgement and decision-making research, no bibliometric review has examined the concept of "harm" in the gambling literature, and little work has quantitatively assessed how gambling research priorities differ between countries. METHODS: Guided by the Conceptual Framework of Harmful Gambling (CFHG), an internationally relevant framework of antecedents to harmful gambling, we conducted a bibliometric analysis focusing on research outputs from three countries with different gambling regulatory environments: Canada, Australia, and New Zealand. Using a Web of Science database search, 1424 articles published from 2008 to 2017 were retrieved that could be mapped to the eight CFHG factors. A subsample of articles (n = 171) containing the word "harm" in the title, abstract, or keywords was then drawn. Descriptive statistics were used to examine differences between countries and trends over time with regard to CFHG factor and harm focus. RESULTS: Psychological and biological factors dominate gambling research in Canada whereas resources and treatment have received more attention in New Zealand. A greater percentage of Australia and New Zealand publications address the gambling environment and exposure to gambling than in Canada. The subset of articles focused on harm showed a stronger harms focus among New Zealand and Australian researchers compared to Canadian-authored publications. CONCLUSIONS: The findings provide preliminary bibliometric evidence that gambling research foci may be shaped by jurisdictional regulation of gambling. Countries with privately operated gambling focused on harm factors that are the operators' responsibility, whereas jurisdictions with a public health model focused on treatment and harm reduction resources. In the absence of a legislated requirement for public health or harm minimisation focus, researchers in jurisdictions with government-operated gambling tend to focus research on factors that are the individual's responsibility and less on the harms they experience. Given increased international attention to gambling-related harm, regulatory and research environments could promote and support more diverse 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 | 0.010 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.092 | 0.109 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".