Examining Ethnic Differences in the Relationship Between Perfectionism and Emotional Adjustment
Bibliographic record
Abstract
Perfectionists are people who set excessively high standards for their own performance, adhere to these standards rigidly, and define their self worth in terms of achieving these standards. Research has shown that perfectionism is linked with emotional and psychological maladjustment. This study is aimed at examining potential differences in perfectionism and emotional adjustment across two ethnic groups: Caucasian and Asian. Previous literature has shown that Eastern cultures are more self criticizing than Western cultures, thus we may expect to see differences in perfectionism across these two cultures. Also, researchers have found that individuals from Eastern cultures may manifest depression in physical symptoms (e.g., headaches) while individuals from Western cultures may express depression in more emotional symptoms (eg, sadness). Thus, differences in how Asians and Caucasians express their emotional maladjustment will be examined. Finally, acculturation may be an important factor because perfectionism in a Western context may have more adverse consequences than in an Eastern context. Method:Asian and Caucasian undergraduate students completed self report measures of perfectionism, emotional adjustment, and acculturation. Expected results: First, we expect that Asians will score higher than Caucasians on perfectionism measures. Secondly, there will be a positive correlation between perfectionism and poor emotional adjustment. Thirdly, emotional adjustment will be expressed more physically in Asians and more emotionally in Caucasians. Finally, within the Asian group, level of acculturation will moderate the relationship between perfectionism and emotional maladjustment. Discussion: These results will be discussed in terms of how perfectionism is conceptualized, assessed, and treated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".