‘No evidence of harm’ implies no evidence of safety: Framing the lack of causal evidence in gambling advertising research
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,469 | 0,627 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,004 |
| Méta-épidémiologie (sens large) | 0,007 | 0,003 |
| Bibliométrie | 0,020 | 0,010 |
| Études des sciences et des technologies | 0,011 | 0,140 |
| Communication savante | 0,028 | 0,050 |
| Science ouverte | 0,012 | 0,022 |
| Intégrité de la recherche | 0,066 | 0,059 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».