Gamblers’ Perceptions of the Impact of the COVID-19 Pandemic on Their Gambling Behaviours: Analysis of Free-Text Responses Collected through a Cross-Sectional Online Survey
Bibliographic record
Abstract
The COVID-19 pandemic has brought drastic changes to the lives of a substantial portion of the world's population. Many stakeholders have expressed concern about the impact of the pandemic on gambling practices, which have historically increased during times of crisis. The purpose of this study was to provide a snapshot of the impact of the pandemic on gambling practices, focusing on the lived experiences of people who gamble. An online cross-sectional survey was conducted between 16 February and 15 March 2021. An open-ended question allowed the participants to describe in their own words the impact of the COVID-19 pandemic on their gambling practices. A qualitative analysis was conducted based on 724 responses to this question. Among the participants, 57% were problem gamblers, according to their Problem Gambling Severity Index score. Three themes were identified: (1) changes in gambling practices perceived by the respondents during the pandemic, (2) the impacts of these changes, and (3) the factors that influenced the changes in their gambling practices. A meaningful proportion of the sample of gamblers felt that their gambling practices had increased during the pandemic. Many of them did not report the deleterious effects of this increase, whereas others were devastated. Thus, variations in gambling practices during the pandemic must be interpreted with caution, as they may reflect a variety of realities.
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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 | 0.003 | 0.009 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".