An Exploratory Study of the Relationship Between Financial Well-Being and Changes in Reported Gambling Behaviour During the COVID-19 Shutdown in Australia
Bibliographic record
Abstract
A change in someone’s financial situation, such as a windfall gain or increased financial stress, can affect the way that they gamble. The aim of this paper was to explore the relationship between financial well-being and changes in gambling behaviour during the coronavirus 2019 (COVID-19) shutdown. Australian past-year gamblers (N = 764; 85% male) completed an online cross-sectional survey in May 2020. Participants retrospectively reported monthly gambling participation before and after the COVID-19 shutdown, as well as their financial well-being, experience of COVID-related financial hardship, problem gambling severity, and psychological distress. Financial well-being showed strong negative associations with problem gambling and psychological distress. Neither financial well-being nor the interaction between financial well-being and problem gambling severity showed consistent evidence for predicting changes in gambling participation during the shutdown in this sample. This study provides preliminary evidence that self-reported financial well-being has a strong negative association with gambling problems but is not related to gambling participation. Future studies should link objective measures of financial well-being from bank transaction data with survey measures of problem gambling severity and experience of gambling-related harm.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".