Are psychosocial interventions effective in minimising harm caused to ‘affected others’ of problem gambling? A Systematic Review
Bibliographic record
Abstract
The large proportions of the population engage in gambling worldwide; United Kingdom 54%, Italy 54%, Malta 58%, and Canada 66.2%. The international data suggest that on average 2.1% of all gambling is problematic. The ‘Problem gambling’ is the behaviour that leads to impaired control over financial and/or time invested in gambling, which in turn results in adverse effects on the individual, their family and/or the community. From one problem gambler, at least seven related individuals are negatively impacted. In this review ‘affected others’ is used as an umbrella term for a variety of individuals impacted by someone else’s gambling addiction. The most common form of harm include; material, relationship breakdown, financial, physical and psychological health and the development of unhealthy behaviours. The efficacy of available interventions aimed at supporting affected others has been questioned.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".