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Enregistrement W4406441193 · doi:10.33684/2024.002

Exploring the problem gambling health-harm paradox

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

Notice bibliographique

Revuenon disponible
Typereport
Langueen
DomainePsychology
ThématiqueGambling Behavior and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesEconomic 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
Mots-clésHarmPsychologyCriminologySocial psychology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,030
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,046

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,030
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,006
Communication savante0,0020,003
Science ouverte0,0010,004
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,627
Tête enseignante GPT0,496
Écart entre enseignants0,130 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission2
Résumé présentoui

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