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Record W4409599660 · doi:10.1177/00169862251328015

Perfectionism, School Burnout, and School Engagement in Gifted Students: The Role of Stress

2025· article· en· W4409599660 on OpenAlexaff
Michael C. Grugan, Luke F. Olsson, Andrew P. Hill, Daniel J. Madigan

Bibliographic record

VenueGifted Child Quarterly · 2025
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerfectionism (psychology)PsychologyBurnoutStress (linguistics)Structural equation modelingDevelopmental psychologySet (abstract data type)Stress managementClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

There is evidence that many gifted students set unrealistically high personal standards and that such perfectionistic tendencies may lead to higher stress. To build on this evidence, we examined whether performance perfectionism and school stress influence school burnout and school engagement in gifted students. A sample of 342 gifted students ( M age = 16.27, SD = 0.49) completed the study measures. Using structural equation modeling, we found that dimensions of performance perfectionism indirectly predicted school burnout and engagement via school stress. When gifted students reported that they expected themselves to perform perfectly at school, or that others expected them to perform perfectly at school, they reported more school stress. In turn, higher levels of school stress were related to increased school burnout and decreased school engagement. The management of performance perfectionism and school stress is therefore important when it comes to supporting and safeguarding gifted students.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.278
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2025
Admission routes1
Has abstractyes

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