Mobilizing social support: New and transferable digital skills in the era of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic is an unprecedented global crisis that has had profound impacts on people’s lives. Under these circumstances, social support can buffer against pandemic-related stress. Yet, the dynamics of the COVID-19 pandemic with its stringent health guidelines have created unique challenges to the mobilization of social support. These challenges particularly affect vulnerable groups with limited digital life skills. Based on a qualitative study of 101 semi-structured interviews with East York residents in Toronto, Canada conducted in 2013–2014, we investigate what new and transferable digital life skills are needed in the pre- and post-pandemic era to mobilize social support. Our findings reveal that East Yorkers easily transfer their digital skills to many spheres of their lives, which help them to organize their busy social lives and coordinate events and gatherings as well as to flexibly socialize online. When needed, East Yorkers adapt and expand their digital skills to substitute for in-person contact, often overcoming communication barriers. One of the key benefits of developing digital life skills is the ability to mobilize social support (i.e., companionship, emotional aid, large services, and technical support), whereby individuals employed different digital skills to mobilize different types of support. The findings demonstrate what new and transferable digital life skills are needed to navigate social support in a post-pandemic era. The study has implications for the development of age-specific interventions to strengthen much needed digital life skills that will aid individuals in mobilizing their social support during crises, such as the COVID-19 pandemic, and help mitigate the negative effects of stress.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".