Substitution behaviors among people who gamble during COVID-19 precipitated casino closures
Bibliographic record
Abstract
The COVID-19 pandemic triggered the closure of licensed casinos throughout the United States of America in March and April 2020. This study sought to examine how Americans who gamble responded to the COVID-19 lockdown, including migration to online gambling, and changes in substance use and use of other technologies. On 9 April 2020, we recruited an online sample of 424 Americans who gambled in the last three months via Amazon’s Mechanical Turk. Self-reported changes in online gambling and other addictive behaviors since the onset of COVID-19 and problem gambling severity were measured. Overall, online gambling decreased following the onset of COVID-19 casino closures, while alcohol, tobacco, and cannabis use increased. Among respondents who reported no online gambling involvement prior to COVID-19, 15% reported migrating to online gambling. These migrators had higher levels of problem gambling and lower income than respondents who had never gambled online. The response to COVID-19 is heterogeneous: the majority of people who gamble reported reducing their online gambling but increased their substance use. A minority of people who gamble substituted casino gambling with online gambling. Because these individuals are characterized by problem gambling symptoms and lower income, they may be considered a vulnerable group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".