Development and Validation of the Perfectionism Literacy Questionnaire
Bibliographic record
Abstract
Perfectionism is a multidimensional personality characteristic associated with mental health problems. However, its features are commonly misunderstood, and many people are unaware of the risks it can pose. This study aimed to develop the first self-report measure of perfectionism literacy. That is, the degree of knowledge someone has about perfectionism, its features and consequences, and when and where to seek help if needed. The Perfectionism Literacy Questionnaire (PLQ) was validated over four stages using four samples of community adults ( N = 1078 total; M age = 37.17 years). In stage one, we generated a pool of items. In stage two, we used exploratory and confirmatory factor analysis to derive a 29-item, seven-factor measure. In stage three, we assessed relationships between the PLQ, perfectionism, and attitudes toward help-seeking for mental health support and found the PLQ is distinguishable from these constructs. In stage four, we examined whether the PLQ was responsive to change following an educational video on perfectionism. We found tentative evidence that minimal intervention can increase perfectionism literacy. Our findings suggest that the PLQ is valid and reliable and may be useful for educational purposes and primary prevention of mental health problems.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".