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Record W4411665209 · doi:10.1080/10615806.2025.2521412

Perfectionism, wellbeing, and coping among Filipino university students: a multi-study test of the 2 × 2 model of perfectionism

2025· article· en· W4411665209 on OpenAlexaff
Jeryl Shawn T. Tan, Andrew P. Hill, Daniel J. Madigan

Bibliographic record

VenueAnxiety Stress & Coping · 2025
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsYork UniversityUniversity of Toronto
FundersYork St John University
KeywordsPerfectionism (psychology)PsychologyCoping (psychology)Clinical psychologyTest (biology)

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.291
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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