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Record W4412760362 · doi:10.1080/28367138.2025.2532411

University Student Eating Disorder Risk and Psychological Distress: Exploring Gender Differences in Academic and Social Factors, Loneliness, University Belongingness, Well-being, and Help-Seeking

2025· article· en· W4412760362 on OpenAlexaffabout
Ken Fowler, Stacey Wareham‐Fowler

Bibliographic record

VenueJournal of College Student Mental Health · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsLonelinessBelongingnessPsychologyPsychological distressDistressClinical psychologySocial psychologyMental healthPsychotherapist

Abstract

fetched live from OpenAlex

Postsecondary eating disorder psychopathology is pervasive, yet studies comparing at-risk female- and male-identified students are scarce. Using the National College Health Assessment (2023) survey, Canadian female- and male-identified students (N = 1,694; mean 26.6 years old) were screened using the SCOFF questionnaire, resulting in eating disorder risk rates of 26.6% and 18.9%, respectively. These students were subsequently assessed in terms of academic, social/relationship, belongingness, well-being, and help-seeking variables, and whether/how these factors predicted psychological distress. Females were more likely to be domestic/undergraduates, reporting problems with family and peers, and higher psychological distress, while males were more likely to be international/graduate students, reporting vigorous physical activity, poorer health, and lower help-seeking. Problems with academics and family, lower BMIs, and higher social media usage predicted female psychological distress, while poorer GPAs, lower physical activity, and weaker university belongingness predicted male psychological distress. Several gender differences emerged with implications for specialized university supports and improved eating disorder screening.

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 score0.149
Threshold uncertainty score0.773

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 integrity0.0000.001
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 head0.051
GPT teacher head0.361
Teacher spread0.311 · 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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