Depressive symptoms among adults is associated with decreased food security
Bibliographic record
Abstract
OBJECTIVE: We aim to evaluate the association of depressive symptoms, depressive symptoms severity and symptom cluster scores (i.e., cognitive-affective and somatic) with food security (FS). We will also evaluate the interaction effect of sex, income and ethnicity on these associations. METHODS: Data from the 2005-2018 National Health and Nutrition Examination Survey cycles were used in this study. Participants included survey respondents 20+ years who had completed Depression and Food Security questionnaires. Multivariable logistic regression was used to estimate the associations between depressive symptoms and FS. RESULTS: A total of 34,128 participants, including 3,021 (7.73%) with depressive symptoms, were included in this study. In both unadjusted and adjusted models, participants with depressive symptoms had lower odds of FS (aOR = 0.347, 95% CI: 0.307,0.391, p<0.001). Moreover, in both unadjusted and adjusted models, for each 1-point increase in cognitive-affective (aOR = 0.850, 95% CI = 0.836,0.864, p <0.001) and somatic symptoms (aOR = 0.847, 95% CI = 0.831,0.863, p <0.001), odds of high FS decreased correspondingly. Our study found no significant interaction effects of sex on depressive symptoms-FS association. Statistically significant interactions of ethnicity and poverty-to-income ratio on depressive symptoms-FS association were observed, revealing higher odds of FS among Non-Hispanic Black and Mexican American groups, and lower odds of FS in Non-Hispanic White and high-income subgroups. CONCLUSION: Our study demonstrated an association between depressive symptoms and decreased FS. Further research is required to deepen our understanding of the underlying mechanisms and to develop focused interventions.
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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 | 0.000 | 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) | 0.001 | 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".