University Student Eating Disorder Risk and Psychological Distress: Exploring Gender Differences in Academic and Social Factors, Loneliness, University Belongingness, Well-being, and Help-Seeking
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".