Exploring clinical perfectionism in higher education students: Key recommendations and reflections on a partnership
Bibliographic record
Abstract
The focus of this paper is two-fold-firstly, we detail the background to our project and provide reflections on the pedagogic partnership.The second focus is the key recommendations arising from our project. BACKGROUNDClinical perfectionism, or maladaptive perfectionism, is characterised by a reliance on "self-evaluation and the determined pursuit (and achievement) of self-imposed personally demanding standards of performance in at least one salient domain, despite the occurrence of adverse consequences" (Shafran et al., 2002, p. 1).Among 992 undergraduate psychology students attending York University (in Canada), the prevalence of perfectionistic cognitions was 25% (Pirbaglou et al., 2013), indicating perfectionistic tendencies are a concern in higher education (HE) settings.Maladaptive perfectionism has been linked to diagnosable mental health disorders (Shafran et al., 2002) and academic-specific elements such as performance indicators, motivation, and academic self-efficacy (Rice et al., 2016).As outlined in Shafran et al. (2002), quantitative measures have been developed to assess perfectionism; however, the definitions on which these are based have varied.Therefore, there is the risk of allowing these measures to define the concept; hence, an exploratory, qualitative approach offers an alternate perspective of perfectionism, as defined by the individual who experiences it.Considering the lived experience of perfectionism in HE students and its subsequent impact on academic study, the dean for diversity and inclusion deemed it necessary to research the impact of perfectionism on students at the University of Reading.This is because the University of Reading is focusing on supporting the mental health of students and, therefore, this study aligns with our institutional strategy.
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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 | 0.062 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.013 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".