Resilience During Uncertainty? Greater Social Connectedness During COVID-19 Lockdown is Associated with Reduced Distress and Fatigue
Bibliographic record
Abstract
Background: Social connections are crucial for our health and well-being. This is especially true during times of high uncertainty and distress, such as during the COVID-19 lockdown. This period was characterized by unprecedented social distancing measures resulting in significant changes to people’s usual social lives. Given the potential effects of this disruption on people’s wellbeing, it is crucial to identify factors which are associated with negative health outcomes, and conversely, those that promote resilience during times of adversity. Aims: We examined the relationship between individuals’ levels of social connectedness during lockdown and self-reported stress, worry, and fatigue. Method: Survey data was collected from 981 individuals in a representative sample of Austrian citizens. Data collection occurred during the last week of a six-week nationwide lockdown due to the COVID-19 pandemic. The final sample consisted of 902 participants. Participants were asked to complete validated questionnaires to assess levels of social connectedness as well as measures of perceived stress, worry—both general and COVID-19 specific—and symptoms of fatigue during the previous two weeks. Results: Our results demonstrate that greater social connectedness during the lockdown period was associated with lower levels of perceived stress, as well as general and COVID-19 specific worries. Furthermore, we found a negative relationship between fatigue and social connectedness, which was mediated by feelings of stress, general worries, and COVID-19 specific worries—respectively, indicating that individuals with smaller network sizes, who were highly distressed during the pandemic, were also likely to report feeling more fatigued. Conclusion: Our findings highlight the important role that social connections play in promoting resilience by buffering against negative physical and mental health outcomes, particularly in times of adversity.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".