A weak scientific basis for gaming disorder: Let us err on the side of caution
Bibliographic record
Abstract
We greatly appreciate the care and thought that is evident in the 10 commentaries that discuss our debate paper, the majority of which argued in favor of a formalized ICD-11 gaming disorder. We agree that there are some people whose play of video games is related to life problems. We believe that understanding this population and the nature and severity of the problems they experience should be a focus area for future research. However, moving from research construct to formal disorder requires a much stronger evidence base than we currently have. The burden of evidence and the clinical utility should be extremely high, because there is a genuine risk of abuse of diagnoses. We provide suggestions about the level of evidence that might be required: transparent and preregistered studies, a better demarcation of the subject area that includes a rationale for focusing on gaming particularly versus a more general behavioral addictions concept, the exploration of non-addiction approaches, and the unbiased exploration of clinical approaches that treat potentially underlying issues, such as depressive mood or social anxiety first. We acknowledge there could be benefits to formalizing gaming disorder, many of which were highlighted by colleagues in their commentaries, but we think they do not yet outweigh the wider societal and public health risks involved. Given the gravity of diagnostic classification and its wider societal impact, we urge our colleagues at the WHO to err on the side of caution for now and postpone the formalization.
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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 | 0.033 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.089 | 0.143 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".