The Relationship and Influencing Mechanism Between Perfectionism and Mental Disorder
Bibliographic record
Abstract
Since 1989, perfectionists and mental disorders have increased simultaneously.With the development of the internet, they have easy access to more dimensions of information, which leads to more aspects of themselves that people can criticize and criticize by others in a rigorous evaluation.The present review organizes that each type of perfectionism that appears in the Multidimensional Perfectionism Scale is specifically associated with a mental disorder, and how other factors that integrate with perfectionism traits can negatively promote mental disorders.For instance, self-oriented and socially prescribed perfectionism promote depression and eating disorder by either focusing on or criticizing only flawed parts of oneself.Also, the other-oriented perfection is particularly related to narcissistic personality disorder because of the similarity such that both traits lead to focus more on themselves.For the limitation, previous research has frequently mentioned the study population's choice.Most research examines experiments on a sample with the following characteristics: Western, educated, industrialized, rich, and democratic (W.E.I.R.D.), which did not illustrate the cultural generality and human universals.A comprehensive and diverse sample is needed for future research to avoid one-sidedness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".