Motivationally-Relevant Domains of Positive Affectivity are Differentially Related to Social Anxiety Symptoms
Bibliographic record
Abstract
Background: This study investigated the extent to which specific facets of positive affectivity (PA) demonstrate differential relationships with social anxiety symptomatology as well as social functioning. Following the conceptual framework of the Broaden and Build theory, as well as prior work demonstrating reward-based linkages to specific PA subdomains, we hypothesized that motivationally-valenced PA facets would show distinct associations with social anxiety and social functioning measures. Methods: Two samples (N = 446 and N = 375) completed self-report measures of PA, social anxiety, internalizing symptoms, and social functioning. Correlational, multiple and logistic regression, and contrast analyses of correlated correlation coefficients were used to identify the presence and magnitude of relationships between PA facets and symptom measures.Results: Relationships between social anxiety and specific subdomains of PA appeared to depend on the motivational relevance of each facet. Specifically, self-assurance was associated with social anxiety symptoms above and beyond other PA facets and negative affect. Additionally, contrast analyses indicated that motivationally-valenced PA facets were stronger predictors than non-motivationally-valenced PA facets for social anxiety symptoms. Conclusions: These results demonstrate a statistically significant divergence between motivationally-valenced subdomains of PA and non-motivationally-valenced subdomains of PA, as they relate to social anxiety symptom severity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".