‘Distant socializing,’ not ‘social distancing’ as a public health strategy for COVID-19
Bibliographic record
Abstract
Social distancing, also referred to as physical distancing, means creating a safe distance of at least two meters (six feet) between yourself and others. This is a term popularized during the COVID-19 pandemic, as it is one of the most important measures to prevent the spread of this virus. However, the term 'social distancing' can be misleading, as it may imply that individuals should stop socializing. However, socializing in a safe context (i.e. over the phone, video-chat, etc.) is especially important during this time of crisis. Therefore, in this narrative review, we suggest the term 'distant socializing' as more apt expression, to promote physical distancing measures while also highlighting the importance of maintaining social bonds. Further, articles discussing the practice, implementation, measurement, and mental health effects of physical distancing are reviewed. Physical distancing is associated with psychiatric symptoms (such as anxiety and depression), suicidal ideation, and domestic violence. Further, unemployment and job insecurity have significantly increased during COVID-19, which may exacerbate these negative mental health effects. Governments, medical institutions, and public health bodies should therefore consider increasing mental health resources both during and after the pandemic, with a specific focus on frontline workers, COVID-19 survivors, and marginalized communities.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".