Parent problem gambling: A systematic review of prevention programs for children
Bibliographic record
Abstract
Parent problem gambling (PG) has pervasive adverse effects on children. These children experience considerable losses such as loss of trust, loss of safety and stability, as well as financial and emotional losses. They are at greater risk for maltreatment and mental health disorders, and they are also at risk for intergenerational transmission of PG. These children are two to four times more likely to develop PG than children of non-PG parents. To date, there has been a dearth of research examining the impact of parent PG on children, and even less research focusing on reducing risks in children of PG parents. The goal of this systematic review was to identify PG prevention programs for children and examine the types of prevention used and whether these programs target specific subgroups. Our search retained 16 studies examining PG prevention programs for children. Results indicated that all of the PG prevention programs in the selected studies are universal and do not target children of PG parents or any other specific subgroups. A large gap is the absence of secondary and tertiary PG prevention programs for children. Another gap is the lack of family focused prevention strategies which the substance use literature has shown to be the most effective form of prevention. Further research is needed on parent PG and ways of reducing risks and increasing protective factors in children and families. A public health framework must be adopted to delay onset, reduce risks and minimize consequences in children of PG parents.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".