Food insecurity is related to eating disorder psychopathology beyond psychological distress in rural Chinese adolescents
Bibliographic record
Abstract
Abstract Objective This study aimed to examine the relationship between food insecurity (FI) and eating disorder psychopathology in a large sample of rural Chinese adolescents. Methods Analyses included 1654 adolescents (55.4% girls; Mage = 16.54 years, SD = 1.45) from a rural high school in southwestern China. FI, eating disorder psychopathology, and psychological distress (i.e., symptoms of depression, anxiety, and stress) were assessed. Data were analyzed by sex. Pearson correlation analysis was performed to investigate the zero‐order association between FI and eating disorder psychopathology. Hierarchical linear regressions were used to explore whether FI could explain meaningful variance in eating disorder psychopathology beyond psychological distress and demographic covariates (e.g., socioeconomic status). Results FI was significantly associated with higher eating disorder psychopathology for boys (r = 0.44, p < 0.001) and girls (r = 0.43, p < 0.001), with medium‐to‐large effect sizes. FI accounted for significant unique variance in eating disorder psychopathology beyond psychological distress and demographic covariates for boys (ΔR2 = 0.14, p < 0.001) and girls (ΔR2 = 0.10, p < 0.001). Discussion Using a large sample of rural Chinese adolescents, this study extends the connection between FI and eating disorder pathology in adolescents beyond the Western context. Future investigations on the mechanisms underlying FI and eating disorder psychopathology are warranted for developing prevention strategies for eating disorders among rural Chinese adolescents. Public Significance This is the first investigation that examined the link between FI and eating disorder psychopathology among rural Chinese adolescents. Our findings highlight the importance of incorporating FI as a potential risk factor to screen for the prevention and intervention of eating disorders among rural Chinese adolescents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".