The impact of internet-based cognitive behavior therapy for perfectionism: a reinterpretation through the lens of the Model of Excellencism and Perfectionism
Bibliographic record
Abstract
Abstract Background: Grieve et al . (2022) tested the effects of an intervention designed to reduce perfectionism. Contrary to their hypotheses, the intervention reduced both perfectionism and excellencism. Furthermore, excellencism positively correlated with negative outcomes (e.g. anxiety). Aims: A theory-driven framework (with five hypothetical scenarios) is proposed to reconsider how we interpret the effectiveness of interventions designed to reduce perfectionism. Our goal was to offer a constructive reinterpretation of the results of Grieve et al . (2022) using our new framework derived from the Model of Excellencism and Perfectionism. Method: Secondary data analyses using the experimental and correlational results are published in the randomized control trial of Grieve et al . (2022). Results: Our re-examination of the results reveals that excellencism was reduced by a smaller extent (approximately 25% less) than perfectionism. Based on our framework, such a ratio provides conclusive evidence for the effectiveness of this intervention. Students entered the intervention as perfectionists and they ended up somewhere between the zones of excellence striving and non-perfectionism. Furthermore, our multivariate re-analysis of the bivariate correlations indicates that excellence strivers experienced better adjustment (lower anxiety, depression, stress, body-related acceptance, and higher self-compassion) compared with perfectionists. Conclusion: Future interventions should target the reduction of perfectionism and the maintenance of excellencism because excellencism relates to desirable outcomes. Our secondary data analysis was needed to inform researchers and practitioners about an alternative interpretation of Grieve and colleagues’ findings. Future interventions to reduce perfectionism should closely monitor excellencism and follow the interpretational guidelines advanced in this article.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".