Bibliographic record
Abstract
Internet addiction is a behavioural problem that has gained increasing scientific recognition in the last decade, with some researchers claiming it is a '21st Century epidemic”, leading the APA to include Internet Gaming Disorder in the appendix of the DSM-5. Internet addiction treatment literature is scarce, particularly regarding the expert view on Internet addiction. To fill this gap in knowledge, this paper aims to explore how Internet addiction therapy experts experience the presenting problem of Internet addiction in psychotherapy. A total of 20 psychotherapists from 6 different countries (i.e., Germany, UK, USA, Canada, Austria and Switzerland) were interviewed regarding their individual experience of treating clients suffering from Internet addiction. Data were analysed using Interpretative Phenomenological Analysis. Two superordinate themes were identified during the analysis: 'risk” and 'addiction”. Risk factors included individual, situational and structural characteristics. Psychotherapists treating Internet addiction viewed Internet addiction as actual psychopathology, containing addiction symptoms, criteria and diagnosis, and drew on its similarities with other addictions. Internet addiction treatment experts highlight the existence and severity of Internet addiction as psychopathology requiring professional therapy. Clients who seek help for their Internet addiction-related problems experience their condition as distressing and as significantly impairing their functioning. Internet addiction fulfils the conditions for a mental disorder classification as outlined in the DSM-5, and should be taken seriously not to marginalise those affected. Parents and significant others, researchers and clinicians, and healthcare and insurance providers may benefit from the presented insights.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".