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Record W4406441193 · doi:10.33684/2024.002

Exploring the problem gambling health-harm paradox

2022· report· en· W4406441193 on OpenAlexfundaboutno aff
B. A. Taylor, Heather Wardle, Isabel Taylor

Bibliographic record

Venuenot available
Typereport
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
FundersEconomic and Social Research CouncilPublic Health EnglandNational Institute on Minority Health and Health DisparitiesAlberta Gambling Research Institute, University of CalgaryNational Institute for Health and Care ResearchJohns Hopkins UniversityGambleAwareWellcome TrustMcGill UniversityHelsingin Yliopisto
KeywordsHarmPsychologyCriminologySocial psychology

Abstract

fetched live from OpenAlex

Purpose: Previous research by NatCen identified a potential health-harm paradox for mental wellbeing and gambling, finding that those with poor mental wellbeing or a diagnosed mental health condition were more likely to experience problem gambling despite being less likely to gamble at all. This report aimed to explore this further, testing three specific hypothesis which could account for this association: 1. That people with poorer mental wellbeing who gamble do so more frequently and it is this increased frequency of gambling that drives elevated rates of gambling severity. 2. That people with poorer mental wellbeing who gamble generally take part in higher risk health behaviours (e.g., higher-risk alcohol consumption; cigarette smoking) and this drives the association. 3. That people with poorer mental wellbeing who gamble are more likely to take part in specific types of gambling that are associated with higher rates of harms. This report explores these potential mechanisms, using data collected in recent Health surveys across England and Scotland. Methodology: Data from the 2015-2017 Scottish Health Survey and the 2015, 2016 & 2018 Health Survey for England were combined, and bivariate analysis was conducted first to confirm that the relationships between mental health, moderate risk/problem gambling and gambling patterns did not vary significantly between survey years. With this established, binary logistic regressions using the combined data from both the Scottish and English Health Survey series were employed to investigate explanatory factors of the association between mental health and experiences of moderaterisk/problem gambling. These include gambling and other health-related factors. These were first conducted on the full sample, and then separate models were estimated for men and women to provide further insights by gender. Measures: For the exposure variable, three different established measures of mental health were used: doctor diagnosis of a mental health condition, the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS; a score of 40 or below indicating probable depression) and the General Health Questionnaire (GHQ-12; a score of 4 or more indicating significant mental distress). The outcome variable of moderate risk/problem gambling was measured by the Canadian Problem Gambling Index’s Problem Gambling Severity Index (PGSI), while gambling activities and frequency were derived from multiple questions in the combined surveys. The PGSI is a measure of the riskiness of a person’s gambling habits. Someone identified as a moderate risk gambler experiences a moderate level of problems with their gambling which can lead to some negative consequences. These might be spending more than they can afford, losing track of time while gambling, or feeling guilty about how much they gamble. A person identified as a problem gambler in the PGSI score will also face negative consequences from their gambling, as well as a possible loss of control. Control variables included socio-demographic characteristics and alcohol and cigarette consumption. Results: Our results confirm previous findings that people with poor mental wellbeing or a diagnosed mental health condition were significantly more likely to experience moderate risk or problem gambling despite being less likely to gamble at all. For example, 50% of those experiencing significant mental distress gambled in the past year compared with 54% of those not experiencing significant mental distress, yet rates of experiencing moderate risk/problem gambling respectively were 2.4% and 1.2%. Logistic regression models showed that the associations between moderate risk and problem gambling and both ‘mental distress’ (measured in the GHQ) and ‘probable depression’ (the WEMWBS) were not fully accounted for by differences in gambling frequency, gambling activity or engagement in other risky health behaviours. Thus, these alternative explanations for the association between mental wellbeing and moderate risk or problem gambling was not supported by any of the three hypotheses tested. In the fully adjusted models, controlling for all these alternative explanations, the odds ratio of moderate risk/problem gambling were 1.86 times higher among those with probable depression and 2.56 times higher among those with significant mental distress. This relationship is not explained by those with poor mental wellbeing who gamble participating in gambling more often, engaging in higher-risk health behaviours or in specific types of gambling activity. However, when looking at doctor-diagnosed mental health conditions and moderate risk or problem gambling, only one hypothesis was rejected – that increased gambling frequency could explain this association. When cigarette smoking and high-risk alcohol consumption were controlled for, and when engagement in specific gambling activities were taken into account, there was no evidence of an association between doctor-diagnosed mental health conditions and moderate risk/problem gambling Finally, gender-stratified analysis revealed that for men with poor mental wellbeing or a diagnosed mental health condition had a similar relationship to moderate risk/problem gambling as the whole population. For women, the study was underpowered to look at this in detail. Implications: Findings from this research suggest that the relationship between poor mental wellbeing and moderate risk/problem gambling is not driven by differences in gambling or other high-risk health behaviour, particularly amongst people experiencing anxiety or depression. The association between poor mental wellbeing and experiences of moderate risk and problem gambling in this data persists when these behaviours are taken into account, though the direction of any causal (or potentially reciprocal) relationship has not been established in this cross-sectional data. Although other factors not measured in study may explain this relationship, it is concerning that people with poor mental wellbeing are more likely to experience gambling harms, despite lower levels of gambling engagement. Therefore, gambling should be embedded within broader strategies for improving mental health (for example, as a risk factor for poor mental wellbeing; for suicidality etc.) and should be considered as both a potential cause and consequence of moderate risk and problem gambling, to help to identify and address this. It also highlights the need for additional research into the specific doctor-diagnosed mental health conditions that increase the risk of moderate risk/problem gambling, as well as further investigations into the causal pathway between both mental distress and depression and moderate risk/problem gambling.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.006
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.627
GPT teacher head0.496
Teacher spread0.130 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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