Family and Friend Communication over Distance in Canada During the COVID-19 Pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, communication technologies have allowed people to maintain connections with their loved ones over distance. At the same time, we do not yet have a deep understanding of if and how communication needs amongst family and friends change as a result of physical distancing and travel restrictions and how technologies could be better designed to support these needs. For these reasons, we conducted an exploratory study to investigate the use of communication technologies and family communication needs during the first fourth months of the COVID-19 pandemic in Canada. We used contextual interviews with 18 participants and an open-ended survey with 12 respondents. Our results show that people began the pandemic with a period of shifting and trialing new communication practices; this included increased communications with family and friends. People tried to recreate in-person situations with large group video calls beyond the typical two-household connection found pre-pandemic. This created challenges related to control and participation, and saw people explore ways to increase a sense of shared atmosphere over distance with efforts to increase physicality. Yet large amounts of technology use generally did not persist as participants abandoned many forms of online interaction over time in a form of technology detachment and sometimes cleanse. These results point to design lessons for times of extreme disconnection between family and friends, such as during a pandemic, where control, participation, and atmosphere receive deep consideration.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".