Advances in problematic usage of the internet research – A narrative review by experts from the European network for problematic usage of the internet
Bibliographic record
Abstract
Global concern about problematic usage of the internet (PUI), and its public health and societal costs, continues to grow, sharpened in focus under the privations of the COVID-19 pandemic. This narrative review reports the expert opinions of members of the largest international network of researchers on PUI in the framework of the European Cooperation in Science and Technology (COST) Action (CA 16207), on the scientific progress made and the critical knowledge gaps remaining to be filled as the term of the Action reaches its conclusion. A key advance has been achieving consensus on the clinical definition of various forms of PUI. Based on the overarching public health principles of protecting individuals and the public from harm and promoting the highest attainable standard of health, the World Health Organisation has introduced several new structured diagnoses into the ICD-11, including gambling disorder, gaming disorder, compulsive sexual behaviour disorder, and other unspecified or specified disorders due to addictive behaviours, alongside naming online activity as a diagnostic specifier. These definitions provide for the first time a sound platform for developing systematic networked research into various forms of PUI at global scale. Progress has also been made in areas such as refining and simplifying some of the available assessment instruments, clarifying the underpinning brain-based and social determinants, and building more empirically based etiological models, as a basis for therapeutic intervention, alongside public engagement initiatives. However, important gaps in our knowledge remain to be tackled. Principal among these include a better understanding of the course and evolution of the PUI-related problems, across different age groups, genders and other specific vulnerable groups, reliable methods for early identification of individuals at risk (before PUI becomes disordered), efficacious preventative and therapeutic interventions and ethical health and social policy changes that adequately safeguard human digital rights. The paper concludes with recommendations for achievable research goals, based on longitudinal analysis of a large multinational cohort co-designed with public stakeholders.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".