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Record W3217475017 · doi:10.3389/fnbeh.2021.786358

Delay Discounting in Established and Proposed Behavioral Addictions: A Systematic Review and Meta-Analysis

2021· review· en· W3217475017 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFrontiers in Behavioral Neuroscience · 2021
Typereview
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and AlcoholismNational Institutes of HealthPeter Boris Centre for Addictions Research
KeywordsAddictionDelay discountingPsychologyMeta-analysisDiscountingBehavioral addictionClinical psychologyCategorical variableAddictive behaviorImpulsivityPsychiatryMedicineStatisticsMathematicsInternal medicine

Abstract

fetched live from OpenAlex

Steep delay discounting, or a greater preference for smaller-immediate rewards over larger-delayed rewards, is a common phenomenon across a range of substance use and psychiatric disorders. Non-substance behavioral addictions (e.g., gambling disorder, internet gaming disorder, food addiction) are of increasing interest in delay discounting research. Individual studies have reported steeper discounting in people exhibiting various behavioral addictions compared to controls or significant correlations between discounting and behavioral addiction scales; however, not all studies have found significant effects. To synthesize the published research in this area and identify priorities for future research, we conducted a pre-registered systematic review and meta-analysis (following PRISMA guidelines) of delay discounting studies across a range of behavioral addiction categories. The final sample included 78 studies, yielding 87 effect sizes for the meta-analysis. For studies with categorical designs, we found statistically significant, medium-to-large effect sizes for gambling disorder (Cohen’s d = 0.82) and IGD ( d = 0.89), although the IGD effect size was disproportionately influenced by a single study (adjusted d = 0.53 after removal). Categorical internet/smartphone studies were non-significant ( d = 0.16, p = 0.06). Aggregate correlations in dimensional studies were statistically significant, but generally small magnitude for gambling ( r = 0.22), internet/smartphone ( r = 0.13) and food addiction ( r = 0.12). Heterogeneity statistics suggested substantial variability across studies, and publication bias indices indicated moderate impact of unpublished or small sample studies. These findings generally suggest that some behavioral addictions are associated with steeper discounting, with the most robust evidence for gambling disorder. Importantly, this review also highlighted several categories with notably smaller effect sizes or categories with too few studies to be included (e.g., compulsive buying, exercise addiction). Further research on delay discounting in behavioral addictions is warranted, particularly for categories with relatively few studies.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.241
GPT teacher head0.463
Teacher spread0.221 · 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