Addressing Gambling Harm to affected others: A scoping review (part II: Coping, assessment and treatment)
Bibliographic record
Abstract
Public health definitions of gambling-related harm include risks to family members and friends. This scoping review broadly aims to identify recent research on addressing gambling harm to adult affected others, focusing on 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, with 82 focusing on coping strategies (22.3 %), assessment (21.5 %), and treatment (39.7 %). Findings revealed affected others employ various coping strategies, which can be gambler- or family-focussed, before accessing other forms of support. Common strategies include financial strategies and informal support but few studies have assessed their effectiveness. Few brief fit-for-purpose instruments with adequate psychometric evaluation are available to assess affected other status, harm, coping, social support, and help-seeking. Affected others are under-represented in treatment (8 % in general practices, 15 %-26 % in online gambling services, 30 %-43 % in gambling helplines), largely due to various barriers, including a lack of service awareness and shame. Low-intensity internet-delivered interventions show promise and can reach affected others who would not otherwise receive professional help. Other affected other interventions, which can be gambler- and/or family-focused, demonstrate good acceptability but somewhat limited efficacy, while couple interventions demonstrate some promising outcomes, although more rigorous evaluations are needed. The diverse treatment needs and preferences of affected others, coupled with the relatively limited efficacy of current treatments, highlight the need for the development of tailored interventions. The findings of this review can be used to inform clinical, research, and policy decision-making.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".