Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance
Bibliographic record
Abstract
As a response to the COVID-19 pandemic, many governments have introduced steps such as spatial distancing and "staying at home" to curb its spread and impact. The fear resulting from the disease, the 'lockdown' situation, high levels of uncertainty regarding the future, and financial insecurity raise the level of stress, anxiety, and depression experienced by people all around the world. Psychoactive substances and other reinforcing behaviors (e.g., gambling, video gaming, watching pornography) are often used to reduce stress and anxiety and/or to alleviate depressed mood. The tendency to use such substances and engage in such behaviors in an excessive manner as putative coping strategies in crises like the COVID-19 pandemic is considerable. Moreover, the importance of information and communications technology (ICT) is even higher in the present crisis than usual. ICT has been crucial in keeping parts of the economy going, allowing large groups of people to work and study from home, enhancing social connectedness, providing greatly needed entertainment, etc. Although for the vast majority ICT use is adaptive and should not be pathologized, a subgroup of vulnerable individuals are at risk of developing problematic usage patterns. The present consensus guidance discusses these risks and makes some practical recommendations that may help diminish them.
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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 | 0.026 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.020 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".