Health Response to Problematic Usage of the Internet: A Global Survey on Trends, Available Treatments and Key Challenges
Bibliographic record
Abstract
Abstract Background and Aims Problematic usage of the internet (PUI) is a growing concern as technology evolves, with over 5.3 billion individuals, including children, using the internet globally. While specific forms of PUI, such as those involving online gaming and gambling, have been recognized as disorders in major diagnostic manuals, there remains a lack of global data regarding prevalence, treatment and health responses to PUI. This study aimed to examine the magnitude, treatment and health responses to PUI at an international level and identify gaps in knowledge. Methods We conducted a global survey within the International Society of Addiction Medicine’s Global Expert Network (ISAM-GEN), involving addiction societies from 38 countries across Europe (13), Asia/Oceania (12), the Americas (8) and Africa (5). Response to PUI was assessed across various domains, including non-specific PUI and problematic online gaming, problematic online gambling, problematic online pornography, problematic social media use and problematic online buying/shopping. The survey structure included sections on six case scenarios representing different PUI subtypes, each followed by targeted questions, along with an evaluation of the significance of PUI, country-level health responses to PUI and the perceived severity of specific PUI subtypes. Results Problematic online gambling (94.8%) and online gaming (86.9%) emerged as the most frequently reported forms of specific PUI, categorized as either frequent or occasional. These were followed by problematic use of social media (84.2%), online pornography (68.3%) and online buying/shopping (52.6%). Psychotherapeutic approaches, such as cognitive behavioral therapy, were identified as the most widely available treatments for PUI, accessible in over 70% of countries surveyed. Despite increasing global attention to PUI, reflected in the establishment of PUI-focused interest groups in 44.7% of the surveyed societies, significant gaps remain. These gaps include the absence of professional certifications, reported by 78.9% of societies, insufficient educational plans for practitioners (68.4%) and a perceived lack of expert training programs (63.2%). Such deficiencies are concerning given that 65.8% of societies emphasized the projected 10-year severity of PUI as either extremely or very important. Conclusion While not as rigorous as representative community surveys, this survey and its findings highlight the global importance of PUI and suggests critical gaps in healthcare responses. The disparity between awareness of PUI’s significance and the limited resources to address it warrant urgent interventions internationally. Future efforts should focus on enhancing training programs and investing in sustainable solutions to monitor and mitigate the growing burden of PUI worldwide.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".