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Record W4400641274 · doi:10.1016/s2468-2667(24)00126-9

The prevalence of gambling and problematic gambling: a systematic review and meta-analysis

2024· review· en· W4400641274 on OpenAlexaff
Lucy Thi Tran, Heather Wardle, Samantha Colledge‐Frisby, Sophia Taylor, Michelle Lynch, Jürgen Rehm, Rachel A. Volberg, Virve Marionneau, Shekhar Saxena, Christopher Bunn, Michael Farrell, Louisa Degenhardt

Bibliographic record

VenueThe Lancet Public Health · 2024
Typereview
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsCentre for Addiction and Mental Health
FundersNational Drug and Alcohol Research CentreNational Institute of Allergy and Infectious DiseasesNational Health and Medical Research CouncilNational Institutes of HealthMedical Research CouncilDepartment of Health and Aged Care, Australian GovernmentAustralian Government
KeywordsMeta-analysisMEDLINEPsychiatryPsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Gambling behaviours have become of increased public health interest, but data on prevalence remain scarce. In this study, we aimed to estimate for adults and adolescents the prevalence of any gambling activity, the prevalence of engaging in specific gambling activities, the prevalence of any risk gambling and problematic gambling, and the prevalence of any risk and problematic gambling by gambling activity. METHODS: We performed a systematic review and meta-analysis. We systematically searched for peer-reviewed literature (on MEDLINE, Embase, and PsycInfo) and grey literature to identify papers published between Jan 1, 2010, and March 4, 2024. We searched for any gambling, including engagement with individual gambling activities, and problematic gambling data among adults and adolescents. We included papers that reported the prevalence or proportion of a gambling outcome of interest. We excluded papers of non-original data or based on a biased sample. Data were extracted into a bespoke Microsoft Access database, with the Joanna Briggs Institute Critical Appraisal Tool used to identify the risk of bias for each sample. Representative population survey estimates were firstly meta-analysed into country-level prevalence estimates, using metaprop, of any gambling, any risk gambling, problematic gambling, and by gambling activity. Secondly, population-weighted regional-level and global estimates were generated for any gambling, any risk gambling, problematic gambling, and specific gambling activity. This review is registered on PROSPERO (CRD42021251835). FINDINGS: We screened 3692 reports, with 380 representative unique samples, in 68 countries and territories. Overall, the included samples consisted of slightly more men or male individuals, with a mean age of 29·72 years, and most samples identified were from high-income countries. Of these samples, 366 were included in the meta-analysis. Globally, 46·2% (95% CI 41·7-50·8) of adults and 17·9% (14·8-21·2) of adolescents had gambled in the past 12 months. Rates of gambling were higher among men (49·1%; 45·5-52·6) than women (37·4%; 32·0-42·5). Among adults, 8·7% (6·6-11·3) were classified as engaging in any risk gambling, and 1·41% (1·06-1·84) were engaging in problematic gambling. Among adults, rates of problematic gambling were greatest among online casino or slots gambling (15·8%; 10·7-21·6). There were few data reported on any risk and problematic gambling among adolescent samples. INTERPRETATION: Existing evidence suggests that gambling is prevalent globally, that a substantial proportion of the population engage in problematic gambling, and that rates of problematic gambling are greatest among those gambling on online formats. Given the growth of the online gambling industry and the association between gambling and a range of public health harms, governments need to give greater attention to the strict regulation and monitoring of gambling globally. FUNDING: Australian National Health and Medical Research Council.

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)0.030
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.083
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.038
Bibliometrics0.0120.012
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
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 head0.658
GPT teacher head0.549
Teacher spread0.109 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations212
Published2024
Admission routes1
Has abstractyes

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