(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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".