Including gaming disorder in the ICD-11: The need to do so from a clinical and public health perspective
Bibliographic record
Abstract
The proposed introduction of gaming disorder (GD) in the 11th revision of the International Classification of Diseases (ICD-11) developed by the World Health Organization (WHO) has led to a lively debate over the past year. Besides the broad support for the decision in the academic press, a recent publication by van Rooij et al. (2018) repeated the criticism raised against the inclusion of GD in ICD-11 by Aarseth et al. (2017). We argue that this group of researchers fails to recognize the clinical and public health considerations, which support the WHO perspective. It is important to recognize a range of biases that may influence this debate; in particular, the gaming industry may wish to diminish its responsibility by claiming that GD is not a public health problem, a position which maybe supported by arguments from scholars based in media psychology, computer games research, communication science, and related disciplines. However, just as with any other disease or disorder in the ICD-11, the decision whether or not to include GD is based on clinical evidence and public health needs. Therefore, we reiterate our conclusion that including GD reflects the essence of the ICD and will facilitate treatment and prevention for those who need it.
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 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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.021 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".