Impact of the COVID-19 Pandemic on Canadian Social Connections: A Thematic Analysis
Bibliographic record
Abstract
Background: On March 11, 2020, the World Health Organization declared COVID-19 a worldwide pandemic. Responses to the pandemic response disrupted Canadian social connections in complex ways; because social connections are determinants of health and well-being, their disruption could adversely affect health and well-being. Moreover, understanding how pandemics and public health responses affect social connections could inform pandemic recovery strategy and public health approaches designed for future pandemics. The purpose of this study is to understand experiences of pandemic impact on social connections over the pandemic. Methods: A sample of 343 Canadian adults was recruited through Athabasca University and social media. Participants were predominantly White (81%) and female (88%). After the pandemic onset, participants responded to open-ended questions about the impact of the pandemic on and any changes to social connections at three time points (baseline, and three- and 6 months from study entry). Responses were categorized into epochs by date (April-June 2020 [Spring]; July-August 2020 [Summer]; September 2020-January 2021 [Fall/Winter]). Qualitative thematic analysis was used to code themes for each epoch. Results: Negative impact of the pandemic (37-45%), loss of social connections (32-36%), and alternative means of connection (26-32%) were prominent themes across the epochs. Restrictions to face-to-face connections were largest in spring (9%) and lowest in the Summer (4%). Conversely, participants increasingly reported limited contact or communication into the Fall and Winter (6-12%) as pandemic restrictions in Canada were reinstated. Conclusions: The COVID-19 pandemic threatens social connections, with negative impacts that fluctuated with COVID-19 case rates and subsequent pandemic restrictions. These findings could be used to identify targets for social supports during the pandemic recovery, and to adjust public health strategies for future pandemics that minimize impact on social connections.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 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) | 0.003 | 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 teacher head, 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".