Adolescents’ experiences of psychological treatment for gaming disorder: a qualitative study
Bibliographic record
Abstract
Background: Gaming disorder is a recently recognized psychiatric condition and a growing public health concern, particularly among adolescents. Despite this, there is limited research on what kind of treatment they need. Existing studies are mostly quantitative and offer limited insight into adolescents' lived experiences. Aim: The aim of this qualitative study is to explore how adolescent patients perceive their gaming as a problem and their experiences of psychological treatment for gaming disorder. Method: We used a qualitative descriptive approach and conducted semi-structed interviews with eight male patients (aged 13-19) about their experiences of psychological treatment for gaming disorder at a specialized clinic. The treatment is a combination of cognitive-behavioral therapy (CBT) and family therapy. The interviews were analyzed using thematic analysis. Results: Participants generally reported positive treatment experiences, especially the value of combining family therapy with individual CBT. They appreciated the broad focus of the treatment, which addressed not only gaming but also problems in other life areas such as school, sleep, and family relationships. Notably, most did not describe gaming as their main problem, but they connected their gaming to difficulties in other areas of life. Conclusion: These findings suggest that effective treatment for gaming disorder should address the broader psychosocial context in which gaming occurs. Patients do not always view gaming as their primary problem, and clinicians should be cautious about framing it as such. Instead, gaming should be explored in connection with other life difficulties. It is helpful when clinicians demonstrate knowledge about gaming and avoid coming across as critical of the gaming. Integrating family therapy into CBT-based interventions appears clinically valuable and warrants further exploration.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".