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Record 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 on OpenAlexfundaboutno aff
Amanda Roberts, Steve Sharman, Henrietta Bowden‐Jones

Bibliographic record

VenueAddiction · 2022
Typeletter
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
FundersGambling Research Exchange OntarioGambleAwareWellcome TrustBritish Medical AssociationRoyal SocietyNational Institute for Health and Care ResearchSociety for the Study of Addiction
KeywordsPopulationHarmPsychologyResidenceHarm reductionGovernment (linguistics)DemographyPublic healthEnvironmental healthMedicineSocial psychologySociology

Abstract

fetched live from 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.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.456
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.345
Teacher spread0.300 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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