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Record W1978568515 · doi:10.1111/add.12044

Commentary on Festl <i>et al</i>. (2013): Gaming addiction – how far have we come, and how much further do we need to go?

2013· letter· en· W1978568515 on OpenAlexaboutno aff
Nancy M. Petry

Bibliographic record

VenueAddiction · 2013
Typeletter
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsAddictionHarmThe InternetPsychologyMental healthQuarter (Canadian coin)PsychiatryBehavioral addictionInclusion (mineral)Social psychology

Abstract

fetched live from OpenAlex

Festl et al. 1 are to be complimented for conducting a nationally representative study of gaming addiction in Germany. In contrast to many prior studies in this field, they surveyed a large number of respondents across a vast age range. They came to the conclusion that very few individuals in Germany are ‘addicted’ to gaming, but a small minority, primarily youth, experience some degree of problematic gaming. Their methodology is commendable, and I agree with their conclusions. Below, suggestions are provided for moving the field of internet gaming forward based on the results from this study. As noted in the Introduction of this paper, the DSM-5 will not include gaming addiction—or internet addiction more generally—as a psychiatric disorder. This topic has been highly controversial and publicized widely. On one hand, inclusion of such a diagnosis may help individuals suffering from problems related to internet or gaming use access treatments that may reduce their suffering. On the other hand, the proliferation of psychiatric diagnoses stands to harm individuals with mental health disorders and possibly the individuals themselves. Treatment dollars will be stretched too thin if they are divided among hundreds of conditions, and people will be labeled inappropriately if behavioral patterns on the high end of ‘normal’ become defined as pathological. It is clear from this study 1 that at least some degree of participation in gaming is ‘normal’. In the larger 50 000-person survey from which these 4382 respondents were drawn, 25% played computerized games at least occasionally. Thus, about a quarter of the German population plays computer games, making this a popular activity. Although gaming is popular, rates of problematic gaming appear to be quite low. Of the 25% at least occasional gamers, 3.7% endorsed four or more of the seven items suggestive of gaming-related problems. If one considers that the 3.7% relates to the 25% of the population who were asked the problem gaming questions, the overall population base rate of ‘problematic users’ in this sample is much lower—about 0.9%. (This calculation presumes that the 75% of the population who do not play games would not endorse more than four of the Game Addiction Scale items, probably a safe assumption.) Correspondingly, the proportion of the overall population who would be considered ‘addicted’ is almost non-existent, at far less than 0.05%. These prevalence rates are even lower than those reported in other European samples 2, 3, and differences may relate to the samples surveyed or other design features. Festl et al. 1 conclude that gaming, while popular, does not appear to lead to substantive problems in the vast majority of individuals, at least not as assessed by the Gaming Addiction Short Scale (3). As noted in this paper 1, methods to classify individuals as having a gaming problem are far from established 4-12. Unlike some prior studies that considered endorsement of even a single item to constitute problematic use, this study used more conservative definitions of ‘problems’ and ‘addiction,’ requiring endorsement of at least four or all seven of the scale items, respectively. In examining the actual items of the GAS, one item does not appear to be very discriminating. The item ‘Did you play longer than intended?’ was endorsed far more often than any other item, with means of about 1.5 versus means well under 0.65 for all items. Although this study 1 was not intended to examine patterns of item endorsement, statistical techniques such as item response theory can assess patterns of responses across items. Such analyses may ultimately provide guidance regarding appropriate constructs to include in the assessment of problematic gaming and identify those that may not provide particularly useful information. If gaming addition is a mental disorder, then first and foremost we need methods to assess it reliably and validly. As noted in this paper 1 and prior reports 4-12, numerous scales exist to assess problems with gaming and internet use more generally. The field needs more systematic research and initial consensus statements, derived from empirical data, to establish more clearly its defining characteristics. Assessment in both the general population and clinically affected samples is needed. Study of the association of ‘gaming addiction’ with other psychiatric illnesses can establish if they are unique or reflect different manifestations of a similar disorder. Additionally, longitudinal studies should evaluate the temporal stability of the condition; if many cases subside rapidly (13), then a psychiatric diagnosis is unlikely to be appropriate. Gaining a fuller understanding of the nature, severity, prevalence and temporal stability of this condition is critical before recognizing ‘gaming addiction’ as a psychiatric disorder.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.269
Teacher spread0.253 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations10
Published2013
Admission routes1
Has abstractyes

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