Exploring the Contemporary Research Trends on Perfectionism and Mental Health: A Bibliometric Analysis
Bibliographic record
Abstract
The current bibliometric analysis aimed to analyze scientific output on the relationship between perfectionism and mental health in peer-reviewed journals between 2010 and 2023. A literature search was conducted using the Web of Science (WoS) database, and 705 publications were extracted. The majority of the studies were research articles. The results indicated that depression, anxiety, stress, and eating disorders were some of the most common mental health conditions related to the trait of perfectionism. The annual number of publications covering the topic followed a steady increase over the 13-year period with slight fluctuations. The average number of citations per article was found to be 16.07. Psychology, followed by psychiatry and education, were the most prominent fields covering this topic. The USA, Canada, and Australia were the top three countries contributing to the investigations regarding the relationship between perfectionism and mental health. The most frequently used keywords were perfectionism, depression, anxiety, and stress. It was concluded that studies covering the association between perfectionism and mental health issues have demonstrated a gradual increase in terms of both the number and the diversity of research over the 13 years. Studies aiming to enrich the literature regarding this topic should be encouraged, especially in low and middle-income countries.
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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.197 | 0.237 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".