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Record W3123800048 · doi:10.1111/add.15411

Expert appraisal of criteria for assessing gaming disorder: an international Delphi study

2021· article· en· W3123800048 on OpenAlexaff
Jesús Castro‐Calvo, Daniel L. King, Dan J. Stein, Matthias Brand, Lior Carmi, Samuel R. Chamberlain, Zsolt Demetrovics, Naomi Fineberg, Hans‐Jürgen Rumpf, Murat Yücel, Sophia Achab, Atul Ambekar, Norharlina Bahar, Alex Blaszczynski, Henrietta Bowden‐Jones, Xavier Carbonell, Elda M. L. Chan, Chih‐Hung Ko, Philippe de Timary, Magali Dufour, Marie Grall‐Bronnec, Hae Kook Lee, Susumu Higuchi, Susana Jiménez‐Múrcia, Orsolya Király, Daria J. Kuss, Jiang Long, Astrid Müller, Stefano Pallanti, Marc N. Potenza, Afarin Rahimi‐Movaghar, John B. Saunders, Adriano Schimmenti, Seung‐Yup Lee, Kristiana Siste, Daniel Tornaim Spritzer, Vladan Starčević, Aviv Weinstein, Klaus Wölfling, Joël Billieux

Bibliographic record

VenueAddiction · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsUniversité du Québec à Montréal
FundersNational Research, Development and Innovation OfficeDefence Science and Technology GroupNational Health and Medical Research CouncilAustralian Research CouncilMagyar Tudományos AkadémiaInnovációs és Technológiai MinisztériumNemzeti Kutatási Fejlesztési és Innovációs HivatalWilson FoundationMedical Research CouncilDepartment of Industry, Innovation and Science, Australian GovernmentNemzeti Kutatási, Fejlesztési és Innovaciós AlapMonash UniversityDepartment of Science and Technology, Ministry of Science and Technology, IndiaWellcome TrustNational Center for Responsible GamingAustralian Government
KeywordsDelphi methodDSM-5DelphiPsychologyClinical psychologyMedicinePsychiatryComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Following the recognition of 'internet gaming disorder' (IGD) as a condition requiring further study by the DSM-5, 'gaming disorder' (GD) was officially included as a diagnostic entity by the World Health Organization (WHO) in the 11th revision of the International Classification of Diseases (ICD-11). However, the proposed diagnostic criteria for gaming disorder remain the subject of debate, and there has been no systematic attempt to integrate the views of different groups of experts. To achieve a more systematic agreement on this new disorder, this study employed the Delphi expert consensus method to obtain expert agreement on the diagnostic validity, clinical utility and prognostic value of the DSM-5 criteria and ICD-11 clinical guidelines for GD. METHODS: A total of 29 international experts with clinical and/or research experience in GD completed three iterative rounds of a Delphi survey. Experts rated proposed criteria in progressive rounds until a pre-determined level of agreement was achieved. RESULTS: For DSM-5 IGD criteria, there was an agreement both that a subset had high diagnostic validity, clinical utility and prognostic value and that some (e.g. tolerance, deception) had low diagnostic validity, clinical utility and prognostic value. Crucially, some DSM-5 criteria (e.g. escapism/mood regulation, tolerance) were regarded as incapable of distinguishing between problematic and non-problematic gaming. In contrast, ICD-11 diagnostic guidelines for GD (except for the criterion relating to diminished non-gaming interests) were judged as presenting high diagnostic validity, clinical utility and prognostic value. CONCLUSIONS: This Delphi survey provides a foundation for identifying the most diagnostically valid and clinically useful criteria for GD. There was expert agreement that some DSM-5 criteria were not clinically relevant and may pathologize non-problematic patterns of gaming, whereas ICD-11 diagnostic guidelines are likely to diagnose GD adequately and avoid pathologizing.

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)0.181
metaresearch head score (Gemma)0.186
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.958

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1810.186
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.003
Science and technology studies0.0050.005
Scholarly communication0.0030.004
Open science0.0030.013
Research integrity0.0030.003
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 head0.045
GPT teacher head0.437
Teacher spread0.391 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations230
Published2021
Admission routes1
Has abstractyes

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