Problem gambling during Covid-19
Bibliographic record
Abstract
Background: Problem gambling could progressively grow in a period of isolation due to the COVID-19 pandemic to the possibility of gambling directly from home. Objectives: This pilot study highlights if the problem gambling, during a period of isolation such as that of COVID-19, can be explained by personality or sociodemographic characteristics, therefore it investigates the emotional and impulsive characteristics of problem gamblers and examines whether those who are adults, those who have more years of study or who work are less likely to have problem gambling. Methods: A total of 200 subjects completed an online survey to examine the associations between problem gambling, alexithymia, and impulsiveness. The standardized tools used were the South Oaks Gambling Screen (SOGS), the Toronto Alexithymia Scale (TAS-20), and the Barratt Impulsiveness Scale (BIS-11). Results: Problem gambling was positively correlated with male gender, TAS-20 total score, difficulty describing feelings, externally-oriented thinking, attentional, and nonplanning impulsiveness. Furthermore, there was a significant inverse correlation between higher SOGS scores and fewer years of study. Multivariate analysis showed that age, gender, years of study, BIS-11 total score, attentional impulsiveness, and nonplanning impulsiveness were predictors of gambling. Conclusions: The results of this exploratory research suggest that in a period characterized by a pandemic, problem gambling is associated with some personality and sociodemographic characteristics. Moreover, age, male gender, low levels of study and impulsive characteristics play a decisive role in problem gambling.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".