Conceptual Framework of Harmful Gambling: An International Collaboration Revised September 2015
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although it is seen by many as a form of leisure and recreation, gambling can have serious repercussions for individuals, families, and society as a whole. The harmful effects of gambling have been studied for decades in an attempt to understand individual differences in gambling engagement and the life-course of gambling-related problems. In this publication, we present a comprehensive, internationally relevant conceptual framework of “harmful gambling” that moves beyond a symptoms-based view of harm and addresses a broad set of factors related to population risk, community and societal effects. Interactive factors depicted in the framework represent major themes in gambling that range from specific (gambling environment, exposure, types, and resources) to general (cultural, social, psychological, and biological). The framework has been created by international interdisciplinary experts and stakeholders - including researchers, treatment providers, operators, policy makers, as well as individuals and their families - in order to facilitate an understanding of harmful gambling. It reflects the state of knowledge related to factors influencing harmful gambling; and serves a secondary purpose as a guide for the development of future research programs and education of policy makers on issues related to harmful gambling. Gambling Research Exchange Ontario (GREO) (formerly the Ontario Problem Gambling Research Centre (OPGRC) located in Guelph, Ontario, Canada) has facilitated the development of the Conceptual Framework of Harmful Gambling and will retain responsibility for keeping it up-to-date.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it