COVID-19 Related Shifts in Social Interaction, Connection, and Cohesion Impact Psychosocial Health: Longitudinal Qualitative Findings from COVID-19 Treatment Trial Engaged Participants
Bibliographic record
Abstract
While effective for slowing the transmission of SARS-CoV-2, public health measures, such as physical distancing and stay-at-home orders, have significantly shifted the way people interact and maintain social connections. To better understand how people sought social and psychological support amid the pandemic, we conducted a longitudinal qualitative evaluation of participants enrolled in a COVID-19 treatment trial (N = 30). All participants from the parent trial who consented to being contacted for future research studies were recruited electronically via email, and first-round virtual interviews were conducted between December 2020 and March 2021. Participants who participated in first-round interviews were contacted again, and follow-up interviews were conducted in January–February 2022. The results reported significant shifts in how participants connected to social support, including changes from physical to virtual modalities, and using different social networks for distinct purposes (i.e., Reddit/Facebook for information, WhatsApp for community connection). While having COVID-19, profound loneliness during isolation was described; yet, to mitigate effects, virtual support (i.e., emotional, knowledge-seeking) as well as in-person material support (e.g., groceries, snow-shoveling), were key. Public health efforts are needed to develop interventions that will improve the narratives about mental health challenges related to COVID-19 isolation, and to provide opportunities to share challenges in a supportive manner among social networks. Supporting social cohesion, despite the everchanging nature of COVID-19, will necessitate innovative multimodal strategies that learn from lived experiences across various stages of the pandemic.
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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 | 0.036 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".