Empowering self-critical perfectionistic students: A waitlist controlled feasibility trial of an explanatory feedback intervention on daily coping processes.
Bibliographic record
Abstract
This study of 176 university students tested a single-session explanatory feedback intervention (EFI), derived from the perfectionism coping processes model. Participants with higher self-critical perfectionism completed daily measures of stress appraisals, coping, and affect for 7 days. A randomized control design was used to compare an EFI condition with a waitlist control condition over 4 weeks with individualized feedback delivered one-on-one by student trainees in-person or remotely through videoconferencing. The feasibility of the individualized analyses of each participant's daily data was supported by identifying daily trigger patterns, maintenance tendencies, strengths, common triggers, and best targets for reducing negative mood and increasing positive mood across several stressors for each participant. Participant ratings indicated that the comprehensive feedback was coherent and functional. Participants in the EFI condition, compared to those in the control condition, reported increases in empowerment, coping self-efficacy, and problem-focused coping, as well as decreases in depressive and anxious symptoms. Between-group effect sizes were moderate-to-large. There were reliable improvements in empowerment and depressive symptoms for 56% and 36%, respectively, of participants in the EFI condition. These findings demonstrate the broad applicability, conceptual utility, and effectiveness of the EFI for self-critical perfectionistic individuals. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".