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Record W4200489752 · doi:10.1002/lim2.54

Maladaptive or misunderstood? Dopamine fasting as a potential intervention for behavioral addiction

2021· article· en· W4200489752 on OpenAlexaff
Yi Yang Fei, Peter Anto Johnson, Noor A.L. Omran, A. A. Mardon, John C. Johnson

Bibliographic record

VenueLifestyle Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsUniversity of AlbertaMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsDopamineAddictionHarmPsychologyPsychiatryPsychological interventionBehavioral addictionIntervention (counseling)MedicineClinical psychologyNeuroscienceSocial psychology

Abstract

fetched live from OpenAlex

Abstract In this commentary, we strive to illustrate common misconceptions of the dopamine fasting fad that has become popular among wellness enthusiasts and purported by health gurus. Here, we review the proposed Dopamine fasting technique for managing behavioral addictions as proposed by California psychiatrist Dr. Cameron Sepah. We first summarize correct and incorrect interpretations of what Dopamine fasting involves. Next, we contextualize the role of dopamine as it relates to behavioral modification interventions for addiction. Particularly, we discuss the role of dopamine in behavioral addiction and the effectiveness of cognitive behavioral therapy (CBT) techniques for various addictions which are the basis of the proposed dopamine fasting technique. While we see potential for dopamine fasting to offer significant benefits to individuals, we highlight the limitation of the self‐guided aspect of dopamine fasting, which could pose physical and emotional harm to individuals if the guideline is misinterpreted or misused as the sole treatment for severe disorders which require clinician input. Future studies should aim to assess not only the scientific efficacy of dopamine fasting as a potential treatment approach for behavioral addiction, but also the needs and well‐being of individuals who seek self‐directed treatment from popular media trends.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.366
Teacher spread0.321 · 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 teacher head, not a consensus.

Study designOther design
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

Citations5
Published2021
Admission routes1
Has abstractyes

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