The effect of gambling problems on the subjective wellbeing of gamblers’ family and friends: Evidence from large-scale population research in Australia and Canada
Bibliographic record
Abstract
Background and Aims: Excessive time and money spent on gambling can result in harms, not only to people experiencing a gambling problem but also to their close family and friends ("concerned significant others"; CSOs). The current study aimed to explore whether, and to what extent, CSOs experience decrements to their wellbeing due to another person's gambling. Methods: We analysed data from The Household Income and Labour Dynamics in Australia Survey (HILDA; N = 19,064) and the Canadian Quinte Longitudinal Study (QLS; N = 3,904). Participants either self-identified as CSOs (QLS) or were identified by living in a household with a person classified in the problem gambling category by the PGSI (HILDA). Subjective well-being was measured using the Personal Wellbeing Index and single-item questions on happiness and satisfaction with life. Results: CSOs reported lower subjective wellbeing than non-CSOs across both countries and on all three wellbeing measures. CSO status remained a significant predictor of lower wellbeing after controlling for demographic and socio-economic factors, and own-gambling problems. There were no significant differences across various relationships to the gambler, by gender, or between household and non-household CSOs. Discussion and Conclusions: Gambling-related harms experienced by CSOs was reliably associated with a decrease in wellbeing. This decrement to CSO's wellbeing was not as strong as that experienced by the person with the first-order gambling problem. Nevertheless, wellbeing decrements to CSOs are not limited to those living with a person with gambling problems in the household and thus affect many people.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".