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Record W4410327850 · doi:10.1016/j.eatbeh.2025.101990

Perfectionism and disordered eating in exercise and nutrition professionals: The role of self-compassion

2025· article· en· W4410327850 on OpenAlexafffund
Maryam Marashi, Danika A. Quesnel, Erin K. O’Loughlin, David M. Brown, Catherine M. Sabiston

Bibliographic record

VenueEating Behaviors · 2025
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversity of New BrunswickUniversity of Toronto
FundersFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of CanadaCanada Excellence Research Chairs, Government of Canada
KeywordsPsychologyPerfectionism (psychology)CompassionDisordered eatingSelf-compassionClinical psychologyPsychotherapistMindfulnessEating disorders

Abstract

fetched live from OpenAlex

Disordered eating (DE) is more prevalent among exercise and nutrition professionals (ENPs) which may be partially due to heightened levels of perfectionism. Self-actualizing strategies such as self-compassion may offer protection against DE but are not well-understood among health and wellness professionals. This cross-sectional study investigated the associations between multidimensional perfectionism (self-oriented, socially prescribed, and other-oriented perfectionism) and a two-factor model of DE: (i) weight and shape concerns and (ii) food preoccupation, among ENPs (N = 93; mean age = 33.5; 88.2 % women). Self-compassion was tested as a potential moderating factor. All three dimensions of perfectionism were positively associated with both DE factors. Self-compassion significantly moderated the relationship between self-oriented perfectionism and weight and shape concerns (b = -0.51, SE = 0.22, p = .023). Similarly, self-compassion moderated the relationship between socially prescribed perfectionism and food preoccupation (b = -0.39, SE = 0.19, p = .05). Findings suggest that self-compassion may help reduce the impact of perfectionism on DE risk in ENPs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.307
Teacher spread0.299 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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