Effect of food insecurity on depression, anxiety, and stress among resettled Syrian refugees in Ontario
Bibliographic record
Abstract
Food insecurity has been linked to adverse health outcomes, particularly among vulnerable populations such as refugees. The aim of this study was to assess the prevalence of food insecurity and its association with depression, anxiety, and stress among resettled Syrian refugee parents in Ontario. This was a cross-sectional study with a total of 540 Syrian refugee parents who resided in Ontario for an average of four years and had at least one child less than 18 years who were interviewed. Information about food insecurity was collected based on the question "During the past year, did you ever eat less because there was not enough food or money for food?". Levels of depression, anxiety, and stress were assessed using the Depression Anxiety Stress Scales (DASS-21). Multiple linear regression analyses were performed to evaluate the relationship between food insecurity and depression, anxiety, and stress after adjusting for sociodemographic, migration-, and health-related factors. 44.6% of respondents reported experiencing food insecurity. Of participants, 7.6%, 8.9%, and 8.5% reported severe to extremely severe levels of depression, anxiety, and stress, respectively. Results of the multiple linear regression analysis showed that food insecurity was significantly associated with higher levels of depression (Adjβ = 2.00, p = 0.008), anxiety (Adjβ = 1.53, p = 0.013), and stress (Adjβ = 1.87, p = 0.019). Implementation of effective government interventions and frameworks are essential to reduce food insecurity among resettled Syrian refugees to ultimately improve their mental health outcomes and overall well-being.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".