Physical Appearance Anxiety Impedes the Therapeutic Effects of Video Feedback in High Socially Anxious Individuals
Bibliographic record
Abstract
BACKGROUND: Video feedback (VF) interventions effectively reduce social anxiety symptoms and negative self-perception, particularly when they are preceded by cognitive preparation (CP) and followed by cognitive review. AIMS: In the current study, we re-examined data from a study on the efficacy of a novel VF intervention for individuals high in social anxiety to test the hypothesis that physical appearance anxiety would moderate the effects of VF. METHOD: Data were analyzed from 68 socially anxious participants who performed an initial public speech, and were randomly assigned to an Elaborated VF condition (VF plus cognitive preparation and cognitive review), a Standard VF condition (VF plus cognitive preparation) or a No VF condition (exposure alone), and then performed a second speech. RESULTS: As hypothesized, when appearance concerns were low, both participants who received Elaborated and Standard VF were significantly less anxious during speech 2 than those in the No VF condition. However, when levels of appearance concern were high, neither Elaborated nor Standard VF reduced anxiety levels during speech 2 beyond the No VF condition. CONCLUSIONS: Results from our analog sample suggest the importance of tailoring treatment protocols to accommodate the idiosyncratic concerns of socially anxious patients.
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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 | 0.006 |
| 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) | 0.002 | 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".