Online gambling during the COVID-19 pandemic: do living conditions matter?
Bibliographic record
Abstract
OBJECTIVE: This article is one of the first to examine the impact of the COVID-19 pandemic on online gambling (OG) participation and gambling-related problems in relation to living conditions. The latter include whether people reported being in a significant intimate relationship, their living arrangements (alone vs. cohabitation), and whether they lived with minor children in the household. METHOD: Participants were recruited using a population-based survey conducted in the province of Québec (Canada) during the second year of the pandemic. The sample comprised 4 531 individuals who participated in OG and was representative of online gamblers in the general adult population. Logistic regression analyses were used to identify the effects of the COVID-19 pandemic on individuals' participation in OG and the experience of gambling-related problems differed according to certain living conditions. RESULTS: Online gamblers who were in an intimate relationship generally had less intense OG practices and were less likely to report gambling-related harms and depression symptoms than those who were not in a relationship and who were living alone or cohabiting. Online gamblers living with minor children exhibited a higher proportion of risky and problematic OG practices, gambling-related harms, and anxiety and depression symptoms. A significantly higher proportion of online gamblers living with minor children reported concerns about their OG behaviors since the start of the pandemic, as well as significant changes in their alcohol use and anxiety and depression symptoms. CONCLUSION: The results indicate an unequal impact of the pandemic on mental health and OG practices according to civil status and living conditions. It therefore seems imperative to consider the intricacies of diverse family structures (e.g., single-parent, couple without children, etc.) beyond the context of the pandemic in order to comprehensively grasp the phenomenon under investigation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".