Research Source Library

Every claim in the Safe Bets curriculum that names a probability, an effect size, or a behavioral pattern can be traced to a specific peer-reviewed study, government survey, or institutional report. This page is the consolidated reference: every cited source, organized by curriculum domain, with the one-line note that explains why it matters.

For day-to-day teaching, you do not need to memorize this list. The lesson detail pages each surface their own sources under "External resources." This page is for moments when you need the citation outside of the lesson - in a parent email, an admin meeting, a department adoption pitch, or a media inquiry.

How to use this library

Three common use cases:

• Parent or guardian conversation. A parent challenges a specific claim ("my kid says you told him loot boxes are gambling"). Pull the citation for that claim from the Recognition section and forward the link. The framing the parent receives stops being "the school says" and starts being "this is what the published research finds."
• Admin or board adoption. A district considering Safe Bets often asks for the evidence base. Cite the per-domain summary below; offer the per-lesson detail pages as a deeper dive.
• Media or public record requests. Use the citation list directly. Each entry includes the study, the journal or institution, and the specific finding that powers the lesson.

The full structured citations also appear at /research, organized for browsing and linking individual sources.

Pathway 1: Recognition

Sources that ground the framework, the categorization of youth-facing products, and the structural design analysis.

• Drummond, A. & Sauer, J.D. (2018). "Video game loot boxes are psychologically akin to gambling." Nature Human Behaviour, 2, 530-532. Foundation for the structural argument that loot boxes share gambling characteristics independent of branding.
• Griffiths, M.D. (1995). Adolescent Gambling. Routledge. The original adolescent-specific framework that the stake + chance + prize test traces back to.
• Zendle, D., Meyer, R. & Over, H. (2019). "Adolescents and loot boxes: links with problem gambling and motivations for purchase." Royal Society Open Science, 6(6), 190049. Shows the loot-box / problem-gambling association is stronger in 16-18-year-olds than in adults.
• Wardle, H. & Zendle, D. (2021). "Loot Boxes, Gambling, and Problem Gambling Among Young People." Cyberpsychology, Behavior, and Social Networking, 24(4), 267-274. Adjusted odds ratio of 4.5 for problem gambling among 16-24-year-old loot box buyers.
• Hing, N. et al. (2022). "Adolescent betting on esports using cash and skins." PLOS ONE. 6.2 percent of under-18s engage in esports skin betting; skin bettors are roughly 3x more likely to meet problem gambling criteria.
• King, D.L. & Delfabbro, P.H. (2018). "Predatory monetization features in video games." International Journal of Mental Health and Addiction. The taxonomy that frames in-game randomized purchases as predatory by design.
• Schull, N.D. (2012). Addiction by Design: Machine Gambling in Las Vegas. Princeton University Press. The reference text on how slot machines (and downstream products) are engineered around variable reinforcement, near-miss, and loss-disguised-as-win.
• Newall, P.W.S. (2025). "Sludge, dark patterns and dark nudges: A taxonomy of online gambling platforms' deceptive design features." Addiction. Source of the dark patterns / sludge / dark nudges vocabulary used in Recognition Lesson 4.
• Wardle, H. et al. (2024). The Lancet Public Health Commission on Gambling. Public-health framing of gambling design as a structural rather than individual risk factor.

Pathway 2: Reasoning

Sources that ground the math (probability, EV, vig), the skill-vs-edge argument, the cognitive distortions, and the platform-tactics analysis.

• Cantinotti, M., Ladouceur, R. & Jacques, C. (2004). "Sports betting: Can gamblers beat randomness?" Psychology of Addictive Behaviors, 18(2), 143-147. Expert hockey bettors picked winners above chance but achieved no greater monetary gains - the cleanest empirical statement of "skill exists, profit does not."
• Williams, R.J. & Connolly, D. (2006). "Does learning about the mathematics of gambling change gambling behavior?" Psychology of Addictive Behaviors, 20(1), 62-68. Critical evidence that math knowledge alone does not change behavior. Justifies why the Reasoning unit pairs math with the cognitive distortions.
• Goodie, A.S. & Fortune, E.E. (2013). "Measuring cognitive distortions in pathological gambling: Review and meta-analyses." Psychology of Addictive Behaviors, 27(3), 730-743. Meta-analysis establishing distortions as the largest measurable difference between problem and non-problem gamblers.
• Clark, L. & Wohl, M.J.A. (2022). "Langer's illusion of control and the cognitive model of disordered gambling." Addiction, 117(8). Confirms illusion of control and the gambler's fallacy as the two best-characterized distortions, central to CBT-based treatment.
• Donati, M.A., Chiesi, F. & Primi, C. (2017). "Gambling-Related Distortions and Problem Gambling in Adolescents." Frontiers in Psychology, 8, 2243. The adolescent-male specific evidence that susceptibility to the gambler's fallacy clusters with the other five distortions.
• Habib, R. & Dixon, M.R. (2010). "Neurobehavioral evidence for the 'near-miss' effect in pathological gamblers." Journal of the Experimental Analysis of Behavior, 93(3), 313-328. Neuroimaging evidence that problem gamblers' brains process near-misses as small wins.
• Langer, E.J. (1975). "The illusion of control." Journal of Personality and Social Psychology, 32(2), 311-328. The foundational illusion-of-control study.
• Tversky, A. & Kahneman, D. (1971). "Belief in the law of small numbers." Psychological Bulletin, 76(2), 105-110. The foundational reference for the gambler's fallacy as a representativeness-heuristic error.
• Lopez-Gonzalez, H., Estevez, A. & Griffiths, M.D. (2017). "Controlling the illusion of control: A grounded theory of sports betting advertising in the UK." International Gambling Studies, 18(1), 39-55. Documents that sportsbook marketing explicitly targets the illusion of control.
• Newall, P.W.S. et al. (2020). "Gambling marketing from 2014 to 2018: a literature review." Current Addiction Reports, 6, 49-56. Reviews how "risk-free" and "free bet" framing exploits loss aversion.
• NCAA Sports Wagering Survey (2023). Live in-play betting is the most popular bet type among college students; this is the U.S. evidence behind the live-in-play emphasis in Reasoning Lesson 5.

Pathway 3: Influence

Sources that ground the marketing-saturation argument, the social-norms correction approach, and the masculinity-and-sports-culture analysis. (Influence lessons are stubs in V1; sources are loaded for the future authoring pass.)

• Pitt, H., Thomas, S.L., Bestman, A. et al. (2016). Found 81 percent of children ages 11-16 could name at least one betting brand.
• Di Censo, R. et al. (2024). 95.6 percent of 18-24-year-olds recalled betting ads during the 2022 World Cup.
• Killick, E. & Griffiths, M.D. (2021). Systematic review of 22 studies on sports betting advertising and behavior.
• Deans, E.G. et al. (2017). Documents how marketing saturates young male sports fans through masculinity framing and "insider knowledge" appeals.
• Neighbors, C. et al. (2015). Personalized normative feedback (PNF) RCT showing peer-norm correction reduces gambling behavior at 3 and 6 months.
• Celio, M.A. & Lisman, S.A. (2014). Earlier PNF RCT establishing that single-session normative feedback can change behavior.
• Larimer, M.E. et al. (2012). Brief MI-style feedback session producing larger effect sizes than multi-session group CBT.

Pathway 4: Vulnerability

Sources that ground the adolescent-neuroscience framing, the escalation-cycle model, and the screening / pre-commitment / help-seeking framework. (Vulnerability lessons are stubs in V1; sources are loaded for the future authoring pass.)

• Steinberg, L. (2008). "A Social Neuroscience Perspective on Adolescent Risk-Taking." Developmental Review, 28(1), 78-106. The dual-system model of adolescent reward sensitivity vs immature impulse control.
• Casey, B.J., Jones, R.M. & Hare, T.A. (2008). "The Adolescent Brain." Annals of the New York Academy of Sciences, 1124, 111-126. Documents the developmental gap between limbic and prefrontal maturation.
• Galvan, A. (2010). "Adolescent Development of the Reward System." Frontiers in Human Neuroscience, 4, 6. Sex-specific data on dopamine receptor overproduction in adolescent males.
• Chambers, R.A. & Potenza, M.N. (2003). "Neurodevelopment, Impulsivity, and Adolescent Gambling." Journal of Gambling Studies, 19(1), 53-84. The adolescent vulnerability framework specific to gambling.
• Blaszczynski, A. & Nower, L. (2002). "A pathways model of problem and pathological gambling." Addiction, 97(5), 487-499. The three-pathways model used to explain why different students develop problems for different reasons.
• APA DSM-5 (2013). Gambling Disorder diagnostic criteria. The clinical reference for the warning-signs checklist.
• Stinchfield, R. et al. (2017). "Development and Psychometric Evaluation of the Brief Adolescent Gambling Screen (BAGS)." Frontiers in Psychology, 8, 2204. Three-item screen with hit rate 0.95, sensitivity 0.88, specificity 0.98 - the screening tool we ground the Vulnerability self-assessment in.
• Auer, M. & Griffiths, M.D. (2013). Foundational work on voluntary deposit limits and pre-commitment.
• Eriksen, A. et al. (2023). Meta-analysis of 29 RCTs on CBT for gambling disorder, finding medium effect sizes (Hedges' g = -0.71).

What is not in this library

A few things worth being honest about, mostly to head off misuse:

• No youth-specific peer-reviewed sources on cryptocurrency gambling. This was a gap in our literature review; community and journalistic coverage exist but the academic base is thin.
• No youth-specific peer-reviewed sources on social casino apps. Adult social casino research is robust (Gainsbury and others); youth-specific research has not caught up to product growth.
• PNF interventions have not been validated under age 18 in gambling contexts specifically. The PNF citations above are college-age. The technique works in adolescent alcohol prevention; the gambling-specific adolescent translation is still pending.
• Pre-commitment strategies have not been tested with youth. Adult evidence (Auer & Griffiths, Ladouceur) is solid; the under-25 evidence is not.

When teaching, you can name these gaps directly. Students respond well to "the research on this is still being done; here is what we know so far," and admins respond well to seeing that the curriculum knows where its evidence is thin.

Direct external links

The most-used institutional resources, for fast reference:

• National Council on Problem Gambling (NCPG). ncpgambling.org. Operates 1-800-GAMBLER and publishes screening standards.
• Massachusetts Council on Gaming and Health (MACGH). macgh.org. Source of one of the more current state-level youth sports betting curricula.
• More Than A Game (NC). morethanagame.nc.gov. State public-health portal with youth-specific guidance documents.
• GambleAware (UK). gambleaware.org. Useful for the most current marketing and product-design research, much of which is published in UK academic journals before reaching the U.S. literature.
• Gamblers Anonymous. gamblersanonymous.org. Meeting finder and peer support resources.
• Suicide & Crisis Lifeline. 988 / 988lifeline.org. For any escalation that includes self-harm risk.