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Record W4388616027 · doi:10.1111/add.16369

‘No evidence of harm’ implies no evidence of safety: Framing the lack of causal evidence in gambling advertising research

2023· letter· en· W4388616027 on OpenAlexafffundabout
Philip Newall, Youssef Allami, Maira Andrade, Peter Ayton, Rosalind Baker, Daniel Bennett, Matthew Browne, Christopher Bunn, Reece Bush‐Evans, Sonia Chen, Sharon Collard, Steffi De Jans, Jeffrey L. Derevensky, Nicki A. Dowling, Simon Dymond, Andrée Froude, Elizabeth Goyder, Robert Heirene, Nerilee Hing, Liselot Hudders, Kate Hunt, Richard J. E. James, En Li, Elliot A. Ludvig, Virve Marionneau, Ellen McGrane, Stephanie Merkouris, Jim Orford, Alberto Parrado‐González, Robert Pryce, Matthew Rockloff, Ulla Romild, Raffaello Rossi, Alex Russell, Henrik Singmann, Trudy Smit Quosai, Sasha Stark, Aino Suomi, Thomas B. Swanton, Niri Talberg, Volker Thoma, Jamie Torrance, Catherine Tulloch, Ruth J. van Holst, Lukasz Walasek, Heather Wardle, Jane West, Jamie Wheaton, Leon Y. Xiao, Matthew M. Young, Maria Bellringer, Steve Sharman, Amanda Roberts

Bibliographic record

VenueAddiction · 2023
Typeletter
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsCarleton UniversityCanadian Centre on Substance Use and AddictionGreoMcGill UniversityUniversity of Calgary
FundersEconomic and Social Research CouncilMedical Research CouncilAlberta Gambling Research Institute, University of CalgaryResponsible Gambling FundGambling Research Exchange OntarioHealth Research Council of New ZealandGambleAwareQueensland GovernmentMovember FoundationMinistry of Health, New ZealandDeakin UniversityDepartment of Social Services, Australian GovernmentNational Health and Medical Research CouncilOntario Ministry of Health and Long-Term CareNational Institute for Health and Care ResearchInternational Center for Responsible GamingGovernment of South AustraliaAustralian GovernmentUniversity of BristolHealth and Care Research Wales
KeywordsHarmFraming (construction)PsychologyAdvertisingSocial psychologyCriminologyBusinessEngineering

Abstract

fetched live from OpenAlex

Gambling advertising is a common feature in international jurisdictions that have liberalized gambling. In the Anglosphere, countries such as Australia, New Zealand and the United Kingdom have experienced extensive gambling advertising during the past decade. This advertising is particularly prominent in relation to professional sports and lottery products. More recently, some Canadian provinces and US states have also witnessed a similar rise in gambling advertising. Several European governments, including Belgium, Italy, Netherlands and Spain, have more recently restricted gambling advertising and sponsorship in professional sports, but the UK government did not announce any action on gambling advertising and sponsorship in its 2023 White Paper. In September 2023, the UK's Minister for Sport, Gambling and Civil Society addressed a governmental select committee, stating: ‘We have very much gone on the evidence, and there's little evidence that exposure to advertising alone causes people to enter into gambling harm’ [1]. This is consistent with the position of the main UK gambling industry trade body, which frequently states in the media that there is ‘no evidence’ linking gambling advertising to harm [2]. We are a group of stakeholders writing to say that this is a misleading framing of the underlying evidence base. It would be equally true to say that there is no evidence demonstrating gambling advertising's safety. This supposed lack of causal evidence (a point contested by some academics [3]) is simply an absence of evidence due to methodological difficulties inherent to gambling advertising research. Importantly, there is also no evidence of an absence of an effect. People are exposed to gambling advertising in their daily lives, and yet the majority of the research community lacks access to the gambling operator data which could be used to investigate longitudinal relationships [4]. Causality is often best tested for via well-controlled laboratory experiments, and yet no contrived experiment can recreate the experience of being exposed to—and potentially influenced by—gambling advertising during one's daily life. Despite these methodological challenges regarding causality, gambling researchers have assembled a wealth of evidence on other aspects of gambling advertising. Gambling advertising can be highly prevalent, especially around live sport [5, 6]; features certain distinct types of content which use a variety of psychological hooks [5, 6], and is often perceived poorly by its recipients [5, 6]. Research has also linked self-reported advertising exposure and gambling [7-9], especially among disordered gamblers, and linked the use of wagering inducements to gambling behaviour using data from an on-line gambling operator [10]. Evidence also suggests that the safer gambling messages found in many gambling adverts are unlikely to counteract any potential harms from advertising [11, 12]. In time, econometric analyses might be run to test for causal reductions in gambling harm from various governmental restrictions on gambling advertising. Policy decisions regarding gambling advertising should not necessitate evidence of a direct causal link to change the status quo, as those who argue that gambling advertising is safe have not been held to the same evidential standard.

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)0.469
metaresearch head score (Gemma)0.627
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.531
Threshold uncertainty score0.655

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4690.627
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0200.010
Science and technology studies0.0110.140
Scholarly communication0.0280.050
Open science0.0120.022
Research integrity0.0660.059
Insufficient payload (model declined to judge)0.0040.001

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 head0.487
GPT teacher head0.526
Teacher spread0.039 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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

Citations28
Published2023
Admission routes3
Has abstractyes

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