Association between problem gambling and personality traits: a longitudinal study among the general Norwegian population
Bibliographic record
Abstract
Objective The present study investigates the longitudinal relationship between problematic gambling (PG) and the five factor model’s personality traits using autoregressive cross-lagged models. Methods The data used in the current study was collected by a national survey in 2013 (n = 10,081) and a follow-up study (n = 5,848) in 2015. PG was measured using Canadian Problem Gambling Index (CPGI) while personality was assessed using Mini-International Personality Item Pool (MINI-IPIP). Participants who completed the CPGI and all the personality items during both waves (n = 2,702) were analysed. Results The results show that neuroticism had positive cross-lagged associations with CPGI. In contrast, conscientiousness and agreeableness in 2013 were found to have inverse cross-lagged effect on CPGI in 2015. Finally, openness and extraversion did not have any cross-lagged associations with CPGI. Conclusion PG poses serious negative implications for the involved individuals as well as their associated close social circle. Hence, it is important to understand predictors of PG for prevention purposes. Personality traits are one of the influential frameworks for examining uncontrolled psychopathological behaviors like PG. The study findings offer significant theoretical as well as practical implications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".