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Record W3201307204 · doi:10.4309/jgi.2021.48.7

An Exploratory Study of the Relationship Between Financial Well-Being and Changes in Reported Gambling Behaviour During the COVID-19 Shutdown in Australia

2021· article· en· W3201307204 on OpenAlexfundvenueno aff
Thomas B. Swanton, Martin Burgess, Alex Blaszczynski, Sally Gainsbury

Bibliographic record

VenueJournal of Gambling Issues · 2021
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
FundersGambleAwareGambling Research Exchange OntarioUniversity of SydneyDepartment of Social Services, Australian GovernmentGaming Technologies Association
KeywordsPsychologyShutdownHarmCoronavirus disease 2019 (COVID-19)Financial distressSample (material)FinanceHarm avoidanceExploratory researchDistressSocial psychologyClinical psychologyBusinessFinancial systemPersonalityBig Five personality traitsMedicineSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.412
GPT teacher head0.485
Teacher spread0.073 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes2
Has abstractyes

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