Examining the effectiveness of cognitive bias modification for perfectionism in exploration of the mediating and moderating effects of body dissatisfaction and self-efficacy
Bibliographic record
Abstract
Abstract The relationship between perfectionism, body dissatisfaction, and self-efficacy is unclear. This study attempted to distinguish the relationship between different dimensions of perfectionism and to examine how they relate to body dissatisfaction and self-efficacy. Experiment 1 examined the effectiveness of two types of Cognitive Bias Modification for Interpretation (CBM-I) techniques in the induction of perfectionism. Experiment 2 explored the mediation and moderation effects of perfectionism facets, body dissatisfaction, and self-efficacy in the induction of perfectionism. Participants were randomly assigned to one of the four CBM-I conditions and completed self-report measures of trait and state perfectionism, body dissatisfaction, self-efficacy, as well as a behavioural task that assessed perfectionistic behaviours before and after the CBM-I induction. The results indicated no significant differences in perfectionism between the experimental groups and the control groups following the perfectionism induction. Using baseline participant characteristics, body dissatisfaction was found to mediate socially-prescribed perfectionism and self-efficacy. Self-oriented perfectionism moderated the association between body dissatisfaction and self-efficacy. State perfectionism may not be influenced by a single session (30 trials) of CBM-I training. Treatment targeting body dissatisfaction may enhance self-efficacy in socially-prescribed perfectionists. Further, interventions that decrease self-oriented perfectionism may reduce body dissatisfaction while increasing self-efficacy.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| 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".