(Internet) Gaming Disorder in <i>DSM</i> -5 and <i>ICD</i> -11: A Case of the Glass Half Empty or Half Full: (Internet) Le trouble du jeu dans le <i>DSM</i> -5 et la CIM-11: Un cas de verre à moitié vide et à moitié plein
Bibliographic record
Abstract
Background: Diagnostic and Statistical Manual of Mental Disorders ( DSM-5) included in 2013 Internet gaming disorder (IGD) as a condition for further study, and in 2018, the World Health Organization included gaming disorder (GD) as a mental disorder in the International Classification of Disease ( ICD-11). We aim to compare disorders of gaming in both diagnostic systems using a sample of young adults in Mexico. Methods: Self-administered survey to estimate the prevalence of DSM-5 IGD and ICD-11 GD in 5 Mexican universities; 7,022 first-year students who participated in the University Project for Healthy Students, part of the World Health Organization World Mental Health International College Student Initiative. Cross-tabulation, logistic regression, and item response theory were used to inform on 12- month prevalence of DSM-5 IGD and ICD-11 GD, without and with impairment. Results: The 12-month prevalence of DSM-5 IGD was 5.2% (95% CI, 4.7 to 5.8), almost twice as high as the prevalence using the ICD-11 GD criteria (2.7%; 95% CI, 2.4 to 3.1), and while adding an impairment requirement diminishes both estimates, prevalence remains larger in DSM-5. We found that DSM-5 cases detected and undetected by ICD-11 criteria were similar in demographics, comorbid mental disorders, service use, and impairment variables with the exception that cases detected by ICD-11 had a larger number of symptoms and were more likely to have probable drug dependence than undetected DSM-5 cases. Conclusion: DSM-5 cases detected by ICD-11 are mostly similar to cases undetected by ICD-11. By using ICD-11 instead of DSM-5, we may be leaving (similarly) affected people underserved. It is unlikely that purely epidemiological studies can solve this discrepancy and clinical validity studies maybe needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".