The Role of Self‐focused Attention and Negative Self‐thought in Interview Anxiety: A test of two interventions
Bibliographic record
Abstract
Job interview anxiety is an unpleasant experience for many candidates that can cause them significant distress in the days leading up to the interview. As a result, many candidates are motivated to overcome their feelings of anxiety, however, few empirical studies have examined strategies aimed at lowering nervous jitters. This study looked at the relation between interview anxiety and ‘self‐focused attention’ (the tendency to focus on oneself during the interview), and also negative self‐thoughts during the interview. We tested two training interventions that are grounded in the social anxiety literature, positive imagery (fostering positive self‐images), and field perspective taking (focusing attention externally), which were designed to reduce self‐focused attention and negative thought. Both interventions decreased interview anxiety and increased interview self‐efficacy, but did not affect interview performance scores.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".