“Prevalence of disordered eating and eating disorders among Norwegian university students before and after the COVID-19 pandemic, 2018 and 2022: The SHoT study.”
Bibliographic record
Abstract
OBJECTIVE: The prevalence of eating disorders (EDs) is increasing, but little is known about their trends among university students, particularly following COVID-19. This study examines prevalence in EDs and disordered eating (DE) symptoms among students in 2018 and 2022, focusing on gender and socio-demographic disparities. METHODS: Data were drawn from the Students' Health and Well-being Study (SHoT) in 2018 (n = 50 054) and 2022 (n = 59 544), a large-scale Norwegian survey covering full-time university students aged 18-36. Participants completed self-report measures assessing symptoms of DE (EDS scale) and ED diagnoses, socio-demographic variables, lifestyle, gender identity and financial status. Statistical analyses included logistic regression to assess associations between factors known to be linked to DE and EDs, chi-square tests for group comparisons, and t-tests for continuous variables. RESULTS: From 2018 to 2022, the prevalence of self-reported EDs increased among females (3.5% in 2018 and 4.5% in 2022) and males (0.4% in 2018 and 0.6% in 2022), while gender-diverse students exhibited the overall highest ED rates (around 10% in both surveys). Anorexia nervosa remained the most common ED among females. Daily/almost daily exercise, financial difficulties, loneliness, and living alone were closely linked to EDs, odds ratios (ORs) ranging from 1.3 to 3.7 in females. Similar patterns were seen for female DE cases. Although numbers were low, financial difficulties and particularly loneliness indicated higher risk for DE and any EDs among males. DISCUSSION: The findings suggest a relative rise in ED prevalence among students after the COVID-19 pandemic, while DE symptoms and -cases only changed minimally. These findings highlight the need for enhanced awareness and student mental health services, particularly for gender-diverse individuals, and to improve early detection and intervention strategies among those facing financial hardship and loneliness.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".