Perceptions Underlying Addictive Technology Use Patterns: Insights for Cognitive-Behavioural Therapy
Bibliographic record
Abstract
Cognitive-Behavioural Therapy (CBT) is considered the 'gold standard' in the treatment of addictive disorders related to excessive technology use. However, the cognitive components of problematic internet use are not yet well-known. The aim of the present study was to explore the cognitive components, that according to problematic users, can lead to potential internet addiction. A total of 854 European adults completed an online survey using a mixed-methods design. Internet problems and attachment styles were assessed, prevalence rates estimated, correlations, chi-squared automatic interaction detection, and content analysis were performed. Self-reported addictions to social networking, internet, and gaming had a prevalence between 1.2% (gaming) to 2.7% (social networking). Self-perception of the addiction problem and preoccupied attachment style were discriminative factors for internet addiction. In an analysis of qualitative responses from self-identified compulsive internet users, a sense of not belonging and feeling of disconnection during life events were perceived as causes for internet addiction. The development depended on a cycle of mixed feelings associated with negative thoughts, compensated by a positive online identity. The severity of this behaviour pattern produced significant impairment in various areas of the participants' functioning, suggesting a possible addiction problem. It is suggested that health professionals administering CBT should target unhealthy preoccupations and monitor mixed feelings and thoughts related to internet use to support coping with cognitive distortions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".