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Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

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The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
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Updated: Jun 4, 2025

Transcranial Direct Current Stimulation for Online Gamers
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Use of 4 Open-Ended Text Responses to Help Identify People at Risk of Gaming Disorder: Preregistered Development and

Paweł Strojny1, Ksawery Kapela1, Natalia Lipp2

  • 1Faculty of Management and Social Communication, Jagiellonian University, Krakow, Poland.

JMIR Serious Games
|December 31, 2024
PubMed
Summary
This summary is machine-generated.

Analyzing gamers' text responses with natural language processing (NLP) can supplement traditional gaming disorder scales. This approach offers deeper insights into gaming disorder without increasing assessment time.

Keywords:
NLPPearsonPolishPythonQCLAQuestion-based Computational Language Assessmentcorrelationgaming disorderlanguage model analysismachine learningmental healthnatural language processingopen-endedpsychologicalresponserisktexttransformers-based

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Area of Science:

  • Psychology
  • Computer Science
  • Computational Linguistics

Background:

  • Traditional quantitative research in psychology relies on numerical data, while natural language processing (NLP) offers new avenues for analyzing qualitative data.
  • The growing prevalence of gaming disorder necessitates innovative assessment tools beyond self-report scales.
  • NLP presents an opportunity to complement existing methods by analyzing textual responses for a deeper understanding of gaming disorder.

Purpose of the Study:

  • To evaluate the efficacy of a transformer-based language model in analyzing gamers' text responses as a supplement to traditional rating scales for gaming disorder.
  • To compare the results of a novel 4-question open-ended text assessment with a standard numerical rating scale for gaming disorder.

Main Methods:

  • Participants (n=417) completed a 4-question open-ended text assessment and a numerical rating scale for gaming disorder.
  • Text responses were processed using HerBERT, a transformer-based language model (BERT).
  • Ridge regression was employed to predict gaming disorder scores from vectorized text responses.

Main Results:

  • A moderate Pearson correlation (0.476) was observed between predicted scores and actual scores when using all four text responses.
  • Individual text responses showed correlations ranging from 0.274 to 0.406.

Conclusions:

  • NLP analysis of short, open-ended text responses can enhance the understanding of gaming disorder without additional time commitment.
  • The study confirmed that textual analysis correlates with established rating scales, and incorporating diverse textual data improves model performance.
  • Further refinement of questions is recommended to align more closely with the clinical definition of gaming disorder.