Addressing gambling harm to affected others: A scoping review (Part I: Prevalence, socio-demographic profiles, gambling profiles, and harm)
Bibliographic record
Abstract
Awareness is growing that gambling harm can affect social networks, including family members and friends. This scoping review broadly aimed to examine contemporary research on gambling harm to adult affected others, covering prevalence, socio-demographic profiles, gambling profiles, and harm (Part I); and coping strategies, assessment, and treatment (Part II). A systematic search of electronic databases identified 121 studies published from 2000, 88 of which related to prevalence (9.9 %), socio-demographic profiles (6.6 %), gambling profiles (4.1 %), and harm (71.9 %). Prevalence estimates in the general population ranged from 4.5 %-21.2 %, though these may overstate direct harm by focusing on exposure to problem gambling. Socio-demographic profiles are mixed, but women are more often affected family members and men are more often affected close friends. Affected others also have higher gambling participation and problems than non-affected individuals. Gambling problems harm an average of six others, who experience an average of seven harms, many of which persist beyond problem resolution, resulting in reduced quality of life. Studies consistently identified harm across multiple domains of harm, with emotional and relationship harms the most common, followed by financial and health harms. Harms were consistently identified using measures with and without direct reference to gambling, but equivocal findings were most evident in research employing standardised measures that did not directly reference gambling. There was some discordance in harm perceptions between gamblers and affected others, suggesting differing family experiences. These findings highlight the need for targeted action by governments, industry, researchers, and service providers to protect affected others from gambling-related harm.
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How this classification was reachedexpand
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".