Ambivalent User Needs as a Challenge and Chance for the Design of a Web-Based Intervention for Gaming Disorder: Qualitative Interview Study With Adolescents and Young Adults
Bibliographic record
Abstract
BACKGROUND: In Germany, there are still many young people with gaming disorder (GD) who do not use or cannot access existing treatment services. Given the increasing prevalence of internet use disorders and GD, especially among young people in Germany, there is a need to provide additional low-threshold treatment options that are easily accessible anywhere. Web-based interventions (WBIs) can be used to achieve this goal. OBJECTIVE: The aim of this study was to explore the treatment needs of young people with GD in Germany and derive implications for the development of a self-guided WBI for GD. METHODS: Using a qualitative study design, we conducted a focus group with 3 young male adults and semistructured individual interviews with 3 male adolescents. Data were analyzed using qualitative content analysis. The reporting of this study followed the COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines. RESULTS: Participants' expectations of web-based help in general and of a self-guided WBI for GD revealed a wide variety of sometimes conflicting user needs. For example, by analyzing participants' experiences with successful strategies, we found that external stabilizers (eg, parental control and support group meetings) were helpful in managing GD. However, with regard to a WBI, participants described it as a barrier if the WBI created "too much pressure." On the other hand, "not enough pressure" (ie, not enough external control) was also mentioned as a barrier. The belief that gaming is rewarding and that only equally rewarding activities are successful alternatives to gaming is in tension with the fact that changing problematic gaming behavior can be stressful and not feel rewarding at all. The data also showed that, on the one hand, a WBI should be designed to be attractive (eg, by incorporating gaming elements) but that it should not be too attractive as this, in turn, could trigger GD. CONCLUSIONS: A self-guided WBI for GD should consider and address conflicting user needs. Ambivalence of needs in the face of coping with GD should not be seen as a problem but as a normal part of a change process and, therefore, actively integrated into the WBI concept and storyline. TRIAL REGISTRATION: German Clinical Trials Register DRKS00032334; https://drks.de/search/en/trial/DRKS00032334.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".