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Record W3152828690 · doi:10.33684/2021.002

Examining lottery play and risk among young people in Great Britain

2021· report· en· W3152828690 on OpenAlexfundno aff
Sasha Stark, Heather Wardle, Isabel Burdett

Bibliographic record

Venuenot available
Typereport
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
FundersGambling Research Exchange Ontario
KeywordsLotteryMental healthPsychologyPopularityNorwegianFirthLogistic regressionPsychiatrySocial psychologyDemographyMedicineSociology

Abstract

fetched live from OpenAlex

Purpose & Significance: Despite the popularity of lottery and scratchcards and some evidence of gambling problems among players, limited research focuses on the risks of lottery and scratchcard play and predictors of problems, especially among young people. The purpose of this project is to examine whether lottery and scratchcard participation is related to gambling problems among 16-24 year olds in Great Britain and whether general and mental health and gambling behaviours explain this relationship. Methodology: Samples of 16-24 year olds were pooled from the 2012, 2015, and 2016 Gambling in England and Scotland: Combined Data from the Health Survey for England and the Scottish Health Survey (n=3,454). Bivariate analyses and Firth method logistic regression were used to examine the relationship between past-year lottery and scratchcard participation and gambling problems, assessing the attenuating role of mental wellbeing, mental health disorders, self-assessed general health, and playing other games in past year. Results: There is a significant association between scratchcard play and gambling problems. The association somewhat attenuated but remained significant after taking into account wellbeing, mental health disorders, general health, and engagement in other gambling activities. Findings also show that gambling problems are further predicted by age (20-24 years), gender (male), lower wellbeing, and playing any other gambling games. Implications: Results are valuable for informing youth-focused education, decisions around the legal age for National Lottery products, and the development of safer gambling initiatives for high risk groups and behaviours, such as scratchcard play.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.190
Threshold uncertainty score0.377

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.119
GPT teacher head0.376
Teacher spread0.257 · 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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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