The Conceptual Framework of Harmful Gambling: A revised framework for understanding gambling harm
Bibliographic record
Abstract
BACKGROUND AND AIMS: The Conceptual Framework of Harmful Gambling moves beyond a symptoms-based view of harm and addresses a broad set of factors related to the risks and effects of gambling harmfully at the individual, family, and community levels. Coauthored by international research experts and informed by multiple stakeholders, Gambling Research Exchange (GREO) facilitated the framework development in 2013 and retains responsibility for regular updates and mobilization. This review article presents information about the revised version of the Conceptual Framework of Harmful Gambling completed in late 2018. METHODS: We describe eight interrelated factors depicted in the framework that represent major themes in gambling ranging from the specific (gambling environment, exposure, gambling types, and treatment resources) to the general (cultural, social, psychological, and biological influences). After outlining the framework development and collaborative process, we highlight new topics for the recent update that reflect changes in the gambling landscape and prominent discourses in the scientific community. Some of these topics include social and economic impacts of gambling, and a new model of understanding gambling related harm. DISCUSSION AND CONCLUSIONS: We address the relevance of the CFHG to the gambling and behavioral addictions research community. Harm-based frameworks have been undertaken in other areas of addiction that can both inform and be informed by a model dedicated to harmful gambling. Further, the framework brings a multi-disciplinary perspective to bear on antecedents and factors that co-occur with harmful gambling.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.030 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".