Effects of Pre-Existing Social Anxiety on Mental Health Outcomes During the COVID-19 Pandemic
Bibliographic record
Abstract
Background and objectives: Individuals with social anxiety (SA) have well-established fears of being negatively evaluated and of exposing self-perceived flaws to others. However, the unique impacts of pre-existing SA on well-being and interpersonal outcomes within the stressful context of the pandemic are currently unknown. Design: In a preregistered study that took place in May 2020, we surveyed 488 North American community participants online. Methods: We used multiple linear regression to analyze whether pre-existing SA symptoms predicted current coronavirus anxiety, loneliness, fears of negative evaluation, use of preventive measures, and affiliative outcomes, and whether pre-existing functional impairment and recent COVID-related stressors moderated these relations. Results: Results highlighted the negative effects of pre-existing social anxiety (SA) on current mental health functioning, especially for participants with higher pre-existing functional impairment and greater exposure to COVID-related stressors. Although participants with higher pre-existing SA reported currently feeling lonelier and more fearful of negative evaluation, they also endorsed greater efforts to affiliate with others. Conclusions: High SA individuals may have heightened desire for social support within the isolating context of the pandemic, in which COVID-related social restrictions enable greater avoidance of social evaluation but may also mask the enduring impairment associated with pre-existing SA.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".