Perfectionism, wellbeing, and coping among Filipino university students: a multi-study test of the 2 × 2 model of perfectionism
Bibliographic record
Abstract
Background Perfectionism is an important characteristic among university students given its associations with their wellbeing and coping. One approach to studying student perfectionism is the 2 × 2 model of perfectionism, which examines the interaction between self-oriented perfectionism and socially prescribed perfectionism (SPP). The model is useful for studying student perfectionism, but tests in different cultural contexts remain limited, with some suggesting its hypotheses need modification.Objectives This article builds on existing research by presenting two novel studies that provide the model’s first tests in predicting university student wellbeing and coping in a Filipino context, as well as tests of alternate cultural makeup and aggravating factor hypotheses for SPP’s role.Methods Following preregistered protocols, two independent samples of Filipino university students completed questionnaires measuring variables of interest – one cross-sectionally (N = 294) and one longitudinally (N = 324) with a 3-month follow-up.Results and Conclusion Moderated regression analyses showed support for the model’s hypotheses across both samples depending on the variable. Findings provided clearer support that students with high SPP or a strong belief that others expect perfection are more vulnerable to poorer wellbeing and unhealthy coping, making SPP an aggravating factor in the Filipino context.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".