Maladaptive and Adaptive Perfectionism Impact Psychological Wellbeing Through Mediator Self-Efficacy Versus Resilience
Bibliographic record
Abstract
The transition through university is often challenging and can negatively impact students’ mental health. Identifying protective factors and opportunities aimed at promoting students’ psychological wellbeing is therefore paramount. Maladaptive and adaptive perfectionism have been found to impact students’ psychological wellbeing. However, less is known about how sociocognitive factors such as self-efficacy and resilience impact this relationship. The current study investigated whether self-efficacy and resilience mediate the relationship between maladaptive perfectionism, adaptive perfectionism and psychological wellbeing, controlling for gender, study mode, study method and COVID-19 lockdowns. Australian university students (N = 193; 86.53% female) aged 18–66 (M = 27.80, SD = 10.45) studying full-time (68.39%) and online (72.02%) completed the Frost Multidimensional Perfectionism Scale, the General Self-Efficacy Scale, the Brief Resilience Scale and the Warwick-Edinburgh Mental Wellbeing Scale. The indirect effect of maladaptive perfectionism on psychological wellbeing through self-efficacy was significant (b = -0.09, SE = .03, 95% CI [-0.15, -0.04]). The indirect effect of adaptive perfectionism on psychological wellbeing through self-efficacy was also significant (b = 0.09, SE = .04, 95% CI [0.02, 0.18]). However, resilience did not indirectly impact the relationship between maladaptive perfectionism, adaptive perfectionism and psychological wellbeing. This study demonstrates the importance of building self-efficacy for maladaptive and adaptive perfectionist students and highlights an opportunity for universities to create self-efficacy-based programs to promote students’ psychological wellbeing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".