Related Experiment Video
Updated: Dec 18, 2025

Transcranial Direct Current Stimulation for Online Gamers
Published on: November 9, 2019
The Conceptual Framework of Harmful Gambling: A revised framework for understanding gambling harm
Margo Hilbrecht1,2, David Baxter1, Max Abbott3
11Gambling Research Exchange, Guelph, ON, Canada.
The revised Conceptual Framework of Harmful Gambling (CFHG) offers a comprehensive, multi-level view of gambling harm. It integrates individual, family, and community factors, moving beyond symptom-based approaches for better understanding and intervention.
Area of Science:
- Addiction Research
- Public Health
- Behavioral Science
Background:
- The Conceptual Framework of Harmful Gambling (CFHG) was initially developed in 2013.
- It addresses risks and effects of gambling harm at individual, family, and community levels.
- The Gambling Research Exchange (GREO) facilitated its development and manages updates.
Purpose of the Study:
- To present the revised Conceptual Framework of Harmful Gambling (CFHG) from 2018.
- To detail the framework's multi-level approach to gambling harm.
- To highlight updates reflecting current gambling landscape and research.
Main Methods:
- Description of eight interrelated factors within the framework.
- Outline of the collaborative development and update process.
- Identification of new topics in the 2018 revision, including social/economic impacts and a new harm model.
Main Results:
- The framework includes specific factors (environment, exposure, types, treatment) and general influences (cultural, social, psychological, biological).
- Updates incorporate social and economic impacts of gambling.
- A new model for understanding gambling-related harm is introduced.
Conclusions:
- The CFHG is relevant to gambling and behavioral addictions research.
- It complements harm-based frameworks in other addiction fields.
- The framework provides a multi-disciplinary perspective on gambling harm antecedents and co-occurring factors.
Related Concept Videos
Drug Abuse and Addiction: Pharmacological Phenomena
Framing Effects
Diagnostic and Statistical Manual of Mental Disorders (DSM)
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Models of Health Promotion and Illness Prevention I
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
Self-Presentation: Self-Monitoring and Self-Handicapping

