Perfectionism at Work: An Investigation of Adaptive and Maladaptive Perfectionism in the Workplace among <scp>C</scp> anadian and <scp>T</scp> urkish Employees
Bibliographic record
Abstract
Although perfectionism has been studied extensively in clinical and educational settings, it has been relatively ignored in the work context, despite its potential effect on employee well‐being. Therefore, we examined the impact of perfectionism on work engagement, strain, and burnout using two samples of working adults from C anada and T urkey. Setting high standards was associated with higher engagement and lower strain and cynicism. However, setting high standards did not provide a unique contribution when controlling for conscientiousness, achievement striving, and achievement motivation. Perceived discrepancy between high standards and perceived performance was associated with higher levels of strain and burnout. There was a significant interaction between standards and discrepancy, such that low discrepancy was associated with lower strain than high discrepancy regardless of one's level of standards. Furthermore, high discrepancy was associated with higher strain when standards were low than when standards were high. Workers with high standards and low discrepancy (adaptive perfectionism) experienced lower strain than workers with high standards and high discrepancy (maladaptive perfectionism).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".