The Impact of Problem Gambling on Families and Relationship Partners: A Scoping Literature Review
Bibliographic record
Abstract
It has been determined that family members and relationship partners of persons with gambling disorders face a variety of problems. Since their problems have not yet been summarized in the literature, we conducted a scoping review to address this issue, and focused on the studies of problems faced by family members and partners of those with gambling disorders and studies on the effects of gambling problems on family members and partners. We searched electronic databases (PsycINFO, PubMed, and MEDLINE) and the reference lists of included studies published up to February 15, 2021 and extracted 2,760 studies. These studies were examined for eligibility, and yielded 101 items that met our predefined criteria, all of which were reviewed. Overall, this review found that (1) the presence of gamblers in families was related to increased gambling or other addiction related behaviors among family members; (2) a variety of intra-family conflicts were likely to arise between gamblers and their families and partners; (3) the presence of gamblers in families increased the risk of violence and abuse for family members, partners, and gamblers themselves; and (4) gambling problems generated a variety of physical and mental health problems in gamblers’ families as well as among others in their proximity. In the future, it will be important to establish beneficial support and treatment methods by using the difficulties identified by this review as outcomes in the treatment of gamblers, along with their families and partners.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".