Examining associations among food insecurity, muscularity-oriented disordered eating, and internalizing symptoms in undergraduate students
Bibliographic record
Abstract
Food insecurity (FI) is a serious public health concern linked to negative physical and mental outcomes, including eating disorder symptoms. However, little is known about its relationship with muscularity-oriented disordered eating (MODE), which involves eating behaviours aimed at increasing muscle mass and decreasing body fat. This study examined the association between FI and MODE, as well as the potential indirect effects of depression and anxiety. A sample of 394 undergraduate students (72.34 % women) from a Canadian university completed online self-report measures of FI, MODE, depression, and anxiety. Linear regression analyses indicated that FI was significantly positively associated with MODE, even when controlling for gender. Indirect effects analyses further revealed that this association was partially explained by depression and anxiety in women, suggesting that psychological distress may play a key role in linking FI to MODE. Importantly, our findings provide evidence that MODE is not limited to individuals with greater financial resources to access high-protein diets and supplements. Food insecurity does not preclude engagement in MODE, emphasizing the need for theoretical models and interventions that consider FI as a potential risk factor. Future research should employ longitudinal designs to clarify the temporal relationship between FI, MODE, and mental health factors, as well as explore these associations in more diverse populations.
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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 | 0.003 |
| 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 | 0.001 |
| 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".