A Person‐Centered Perspective on Multidimensional Perfectionism in Canadian and Chinese University Students: A Multigroup Latent Profile Analysis
Bibliographic record
Abstract
This study investigated the generalizability of the tripartite model of perfectionism across Canadian and Chinese university students. Using latent profile analysis and indicators of perfectionistic strivings, perfectionistic concerns, and neuroticism in both groups, the authors derived a 3‐profile solution: adaptive perfectionists, maladaptive perfectionists, and nonperfectionists. Furthermore, multigroup latent profile analysis supported the construct equivalence of the 3‐profile solution across groups. Results further suggested that a greater proportion of Chinese students could be classified as adaptive perfectionists. Este estudio investigó la generalizabilidad del modelo tripartito de perfeccionismo entre estudiantes universitarios canadienses y chinos. Usando un análisis de perfil latente e indicadores de esfuerzos perfeccionistas, preocupaciones perfeccionistas y neuroticismo en ambos grupos, los autores desarrollaron una solución de 3 perfiles: perfeccionistas adaptados, perfeccionistas inadaptados y no perfeccionistas. Además, el análisis de perfil latente multigrupo confirmó la equivalencia de constructo de la solución de 3 perfiles en estos grupos. Los resultados sugieren también que una mayor proporción de estudiantes chinos podría clasificarse como perfeccionistas adaptados.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".