Social Anxiety and Depression in Romantic Relationships: A Three-Sample Exploration.
Bibliographic record
Abstract
Introduction: Social anxiety contributes to a variety of interpersonal difficulties and dysfunctions. Socially anxious adults are less likely to marry and more likely to divorce than are non-anxious adults. The present pre-registered study investigated incremental variance accounted for by social anxiety in relationship satisfaction, commitment, trust, and social support. Methods: Three independent samples of adults (N = 888; 53.7% female; Mage = 35.09 years) involved in a romantic relationship completed online self-report questionnaires. Both social anxiety and depression were significantly correlated with relationship satisfaction, commitment, dyadic trust, and social support. Hierarchical regression analyses were conducted with each sample to investigate the incremental variance accounted for by each of social anxiety and depression in relationship satisfaction, commitment, dyadic trust, and social support. Subsequent meta-analyses were run to determine the strength and replicability of the hierarchical models. Results: Results suggest that social anxiety is a robust predictor of unique variance in both perceived social support and commitment. Depression was a robust predictor of unique variance in relationship satisfaction, dyadic trust, social support, and commitment. Discussion: These results help to further understanding of social anxiety in romantic relationships and provide direction for future research and clinical intervention.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".