How gambling harms others: The influence of relationship-type and closeness on harm, health, and wellbeing
Bibliographic record
Abstract
Background and aims: Concerned significant others (CSOs) can experience gambling-related harm, impacting their health and wellbeing. However, this harm varies depending on the type and closeness of the relationship with the person who gambles. We sought to determine the type and closeness of relationships that are more likely to experience harm from another person's gambling, and examine which aspects of health and wellbeing are related to this harm. Methods: We examined survey data from 1,131 Australian adults who identified as being close to someone experiencing a gambling problem. The survey included information on relationship closeness, gambling-related harm (GHS-20-AO), and a broad range of health and wellbeing measures; including the Personal Wellbeing Index (PWI), the 12-item Short Form Survey (SF-12), and the Positive and Negative Affect Schedule Short Form (PANAS-SF). Results: CSOs in relationships where finances and responsibilities are shared were more likely to be harmed by another person's gambling problem, particularly partners (current and ex) and family members. This harm was most strongly associated with high levels of distress and negative emotions, impacting the CSO's ability to function properly at work or perform other responsibilities. Discussion and Conclusions: Support and treatment services for CSOs should consider addressing the psychological distress and negative emotions commonly experienced by CSOs.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".