The roles of coping style and social support in the experience of harm and distress among people affected by another person’s gambling
Bibliographic record
Abstract
BACKGROUND: Gambling-related harms can negatively impact the health and wellbeing of those around the person who gambles (affected others, AOs). The stress-strain-coping-support (SSCS) model proposes that the type of coping strategies AOs use, and the availability of social support, can effectively reduce some of these negative consequences. The current study aimed to explore the assumptions in the SSCS model by examining the role of coping styles and social support on the experience of harm and psychological distress in AOs. METHOD: A community sample (N = 1,131) of AOs completed the Problem Gambling Severity Index (PGSI), Gambling Harm Scale for Affected Others (GHS-AO-20; harm), Significant Other Closeness Scale, Kessler-6 (K6; psychological distress), Brief Coping Questionnaire (coping styles) and the Multidimensional Scale of Perceived Social Support (social support). Data were analysed using hierarchical multiple regression. RESULTS: The use of maladaptive coping styles was positively associated with harm and psychological distress. Social support was significantly negatively correlated with harm and distress. When all predictors were included in regression analyses, the only significant predictors of harm and distress were being exposed to a more severe gambling problem, being closer to the person with the gambling problem, greater use of maladaptive coping styles, and lower levels of social support. Some interaction effects were identified. CONCLUSIONS: Some commonly used coping behaviours may inadvertently exacerbate harm and distress, while social support appears to be protective against negative health impacts. Education, treatment, and support to AOs could provide options that address these findings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".