Diagnosis of Eating Disorders Among College Students: A Comparison of Military and Civilian Students
Bibliographic record
Abstract
INTRODUCTION: Eating disorders are often under-detected, which poses a serious threat to the health of individuals with eating disorder symptoms. There is evidence to suggest that the military represents a subpopulation that may be susceptible to high prevalence of eating disorders and vulnerable to their underdiagnosis. Underreporting of eating disorder symptoms in the military could lead to this underdiagnosis of individuals with eating disorder symptoms. The purpose of this study was to examine the association between military affiliation and eating disorder symptoms among college students and the likelihood of eating disorder diagnosis among those with eating disorder symptoms using a large, diverse college-aged sample of both military-involved and civilian students. MATERIALS AND METHODS: Participants for this study were from the 2015-2016, 2016-2017, and 2017-2018 Healthy Minds Study (HMS). Healthy Minds Study is a large, cross-sectional cohort study of both undergraduate and graduate students from universities and colleges across the United States and Canada. The Healthy Minds Study survey questions include assessment of demographic information, military status, self-reported eating disorder symptoms using the SCOFF questionnaire, and self-reported eating disorder diagnosis. Univariate analysis, chi-square analysis, and logistic regression with an unadjusted and covariate adjusted model were used to examine the association between eating disorder symptoms and military affiliation. These analyses were also used to examine the association between eating disorder diagnosis among those with eating disorder symptoms and military affiliation. All analyses were conducted using SPSS. RESULTS: The prevalence of eating disorder symptoms was high among both the civilian (20.4%) and military-involved (14.4%) students. Among females, there was a significantly higher (P value = .041) prevalence of eating disorder symptoms among civilian college students (24.7%) compared to military-involved students (21.3%). Among those with eating disorder symptoms, the prevalence of diagnosis was low in both military and civilian students. Specifically, the prevalence of diagnosis was significantly lower (P value = .032) among military-involved college students (10.8%) compared to civilian college students (16.4%). Differences in sociodemographic characteristics (e.g., gender, race/ethnicity, and age) among military-involved and civilian college students appear to explain this association. CONCLUSIONS: The underdiagnosis of eating disorders is far too common, and this represents a threat to the health of military and civilian populations alike. Underdiagnosis of eating disorders within military environments may be due to underreporting, particularly among men and racial/ethnic minority groups.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".