Associations Between Food Insecurity and Depression among Diverse Asian Americans
Bibliographic record
Abstract
Background: Proper nutrition is an essential component to both physical and emotional health. Food insecurity (FI) is a potentially critical public health problem. The link between FI and elevated risk for depression has been well documented. Yet, it is largely unknown how diverse older adult populations experience FI differently. Therefore, the aims of this study were to examine how gender, race/ethnicity, and nativity may impact the magnitude of the association between FI and depression. Methods: We used a nationally representative sample of the Asian American population from the National Latino and Asian American Study (NLAAS). We built logistic regression models with major depression in the past 12 months as the dependent variable, and FI as the independent variable. Several demographic and socioeconomic characteristics were added to the models to control for potential biases. All statistical estimates were weighted, using the recommended NLAAS sampling weight, to ensure representativeness of the US population. Results: About 35% (weighted adjusted 95% CI: 29.49–39.00) of Asian Americans experienced some level of FI at the time of survey. Experiencing FI over the past 12 months increased the likelihood of having clinical depression (weighted adjusted odds ratio: 1.44, weight adjusted confidence interval: 0.79–2.10). The magnitude of associations between FI and depression varied by race/ethnicity (F (7, 47) = 6.53, p (3, 41) = 10.56, p (3, 41) = 9.85). Conclusions: Food insecurity significantly increases the likelihood of clinical depression among Asian Americans. Greater attention is needed towards food-insecure Asian Americans and their mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".