Problem gambling, associations with comorbid health conditions, substance use, and behavioural addictions: Opportunities for pathways to treatment
Bibliographic record
Abstract
BACKGROUND: Problem gambling is a public health issue and its comorbidity with other health conditions may provide an opportunity for screening in healthcare settings; however, a high level of uncertainty and a lack of research in the field remains. The objective of this study is to investigate potential associations between problem gambling and numerous other health conditions, including substance use, mental health problems, and behavioural addictions. METHODS: A cross-sectional web-survey was distributed by a market research company to an online panel of respondents in Sweden, which aimed to be representative of the general population. Chi-squared and Mann-Whitney U tests, followed by logistic regression analysis, were performed to determine associations between screening positive for lifetime problem gambling and potential comorbid conditions and behaviours. RESULTS: Among 2038 participants, 5.7 percent screened positive for lifetime problem gambling. Significant associations were found between problem gambling and male gender, education level, daily tobacco use, moderate psychological distress, problematic shopping, and problem gaming. CONCLUSION: The association between screening for problem gambling and other health conditions, including psychological distress and behavioural addictions such as shopping and gaming, demonstrates the need to screen for problem gambling in the context of other health hazards, such as in different healthcare settings. Further research is required to identify the temporal relationship between these conditions and to investigate underlying etiological mechanisms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".