MétaCan
Menu
Retour à la cohorte
Enregistrement W4220731848 · doi:10.1111/add.15864

The ability of the UK population surveys to capture the true nature of the extent of gambling‐related harm

2022· letter· en· W4220731848 sur OpenAlexfundaboutno aff
Amanda Roberts, Steve Sharman, Henrietta Bowden‐Jones

Notice bibliographique

RevueAddiction · 2022
Typeletter
Langueen
DomainePsychology
ThématiqueGambling Behavior and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesGambling Research Exchange OntarioGambleAwareWellcome TrustBritish Medical AssociationRoyal SocietyNational Institute for Health and Care ResearchSociety for the Study of Addiction
Mots-clésPopulationHarmPsychologyResidenceHarm reductionGovernment (linguistics)DemographyPublic healthEnvironmental healthMedicineSocial psychologySociology

Résumé

récupéré en direct d'OpenAlex

The UK government is undergoing consultation to reform the UK 2005 Gambling Act. Gambling behaviour in the general population was measured via the British Gambling Prevalence Survey (BGPS), (1999, 2007 and 2010) [1] and, since 2010, via the Health Survey England (HSE) and Scottish Health Survey (SHeS) [2], and more recently by small telephone surveys carried out quarterly by the Gambling Commission (GC) [3]. The GC telephone surveys involve only a small non-representative sample and rely upon respondents answering a number they do not recognize. Similarly, although BGPS and HSE data provide a cross-sectional snapshot of gambling behaviour, such surveys are subject to methodological limitations. For example, both surveys exclude people who do not have a residential address, such as those who are experiencing homelessness, and also fail to include people who reside at institutional addresses such as hospitals, prisons, military barracks and student halls of residence. Such populations are likely to have higher rates of gambling problems [4, 5]. As a consequence, both surveys are likely to significantly under-report gambling-related harm. Such methodological limitations are not limited to gambling surveys; a recent article regarding measuring heroin use via general population surveys (the US National Survey on Drug Use and Health) drew the conclusion that such methodological limitations are likely to lead to significant underestimation of the disorder [6]. Similarly, prevalence surveys rely upon subjective self-reports and are prone to error [7], such as selective non-response or selection bias [6, 8] and socially desirable responding [9]. Even the largest surveys have been shown to rely upon the responses of a small number of the overall populace [6]. Research has shown that people may be less likely to take part in research and to disclose problematic gambling for reasons such as stigma [10]. Furthermore, data collected by prevalence surveys are cross-sectional, which do not capture the episodic nature of disordered gambling [11, 12] or the harms experienced beyond the individual. Gambling harms can impact the health and wellbeing of individuals, as well as families, communities and society as a whole [13]. Additionally, both surveys use the Problem Gambling Severity Index (PGSI), which has reliable properties for detecting gambling disorder but is less appropriate for measuring individuals who are ‘at-risk’ of problematic gambling [14], although at-risk gamblers are estimated to account for approximately 85% of the burden of gambling harm at population level [15, 16]. The primary focus of the BGPS was gambling behaviour; however, the number of gambling questions has been reduced in the broader HSE and SHeS [2]. Consequently, key topics which would provide vital evidence are lacking. In addition, the health surveys include gambling questions towards the end of the survey which can reduce data quality, due to decreases in concentration and enthusiasm towards latter sections of a questionnaire [17]. Similarly, positioning gambling questions at the end of a long survey to detect a population with high impulsivity levels is a significant issue, as it is unlikely that respondents work their way consistently to the end [18]. Gambling questions in health surveys have correspondingly been demonstrated to show a much lower prevalence than gambling-specific questionnaires [19]. Akin to the foundation of the formulation of substance use policy, it is crucial that we quantify and recognize the extent of harms attributable to gambling. This is unlikely to be achieved by cross-sectional surveys alone. There is need for a gambling-specific, longitudinal prevalence study that utilizes more comprehensive and inclusive data collection methodologies and more clearly understands the true extent of wider gambling harms. These data can then be triangulated with existing large-scale data sets such as those held by the financial sector, health and social care records and criminal justice systems. Although a large task with multiple obstacles, better cohesion across sectors is essential to move towards a more effective use of data that can support the identification, minimization and prevention of gambling-related harms. The content of this letter is solely the responsibility of the authors. A.R. has received funding from the Society for the Study of Addiction (SSA) and the Gambling Research Exchange Ontario (GREO). S.S. has received funding from the Society for the Study of Addiction (SSA), the King's Prize Fellowship Scheme funded by the Wellcome Trust Institutional Strategic Support Fund and as part of the NIHR Biomedical Research Centre funding for the National Addiction Centre. H.B.-D. is the Director of The National Problem Gambling Clinic which receives funds from the National Health Service and GambleAware. After 21 March 2022 the clinic will only receive funds from the National Health Service. She is also board member of the International Society for the Study of Behavioural Addictions, President of the Royal Society of Medicine Psychiatry Section and Trustee of the RSM Elected Board of Science member at the British Medical Association. Amanda Roberts: Investigation. Steve Sharman: Investigation. Henrietta Bowden-Jones: Conceptualization; investigation.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,456
Score d'incertitude au seuil0,931

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,045
Tête enseignante GPT0,345
Écart entre enseignants0,300 · 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 tête enseignante, 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

Citations7
Publié2022
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueAddictionMême sujetGambling Behavior and TreatmentsTravaux en français237 207