Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it