Intrusive social images in individuals with high and low social anxiety: a multi-method analysis
Bibliographic record
Abstract
BACKGROUND: Models of social anxiety suggest that intrusive images/memories are common in social anxiety and contribute to the maintenance of social anxiety. AIMS: We examined the context and phenomenological features of intrusive social images using quantitative and qualitative measures across various levels of social anxiety. METHOD: Undergraduate students (n = 191) completed measures of social anxiety (i.e. Social Interaction Anxiety Scale and Social Phobia Scale) and wrote a description of an intrusive social image. Individuals who reported an intrusive social image (n = 77) rated the frequency, interference and phenomenological (e.g. vividness, emotional intensity) characteristics of the image. A content analysis of the intrusive image narratives was completed by independent raters. RESULTS: High social anxiety (HSA) increased the likelihood and frequency of experiencing intrusive images, and to some extent the interference caused by these images. However, the characteristics of these images with regard to their content and quality were similar across levels of social anxiety. Among participants who provided narratives, HSA individuals (n = 34) did not differ from low socially anxious (LSA) individuals (n = 28) in themes that reflect concerns about their own thoughts, actions and behaviours. However, HSA individuals reported greater concerns about how other individuals would react, and their intrusive images were often from an observer perspective when compared with LSA individuals. CONCLUSIONS: These results are interpreted in relation to cognitive models of emotion, memory and cognitive behavioural models of social anxiety.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".