Bibliographic record
Abstract
Research suggests that those experiencing Social Anxiety (SA) symptoms are more likely to engage in repetitive thought (RT), including upward counterfactual thinking (U-CFT). Findings indicate that these cognitive patterns may lead to deleterious thoughts and emotions, particularly when U-CFT focuses on non-repeatable, uncontrollable situations and negative self-appraisals. The present dissertation consisted of two complementary studies. Study 1 attempted to 1) validate new measures of state and trait U-CFT, 2) examine the relationship between U-CFT and established measures of RT and mood, and 3) explore the relationship between SA symptoms and counterfactual thinking within a student population. Results indicated that the U-CFT-S (trait measure of U-CFT) and the Counterfactual Likelihood scales (state measure of U-CFT) evidenced sound psychometrics in terms of internal consistency, factor structure, and relationships with related questionnaires. Factor analyses revealed that the Maladaptive U-CFT-S scale clustered with negative mood, rumination, and learned helplessness, while the Adaptive U-CFT subscale clustered with measures of positive mood and self-efficacy. Finally, symptoms of SA correlated positively with state and trait U-CFT generation. Study 2 1) compared patterns of U-CFT and emotions such as guilt and self-blame between a diagnosed Social Anxiety Disorder (SAD) group and a Healthy Control (HC) group 2) determined if disorder-specific content impacts U-CFT generation, and 3) piloted a brief, CBT-based, video intervention targeting maladaptive U-CFT. Results indicated that the SAD group evidenced higher amounts of U-CFT in response to the socially-based scenarios than the HC group and in response to social than non-social scenarios. The SAD group evidenced higher levels of unhelpful emotions (e.g., guilt) both pre- and post-CFT generation than HC participants. Finally, the CBT intervention was generally unsuccessful at reducing maladaptive U-CFT, but was more likely to be effective among SAD than HC participants. Implications of this dissertation include: 1) the benefit of including state- and trait-based measures of U-CFT in future research, 2) the importance of conceptualizing U-CFT as a multifaceted construct, 3) addressing that those with SAD are engaging in maladaptive U-CFT and experiencing consequent guilt and self-blame, and 4) the direction of creating more comprehensive, brief interventions aimed at targeting maladaptive U-CFT.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".