Food worry and mental health outcomes during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: There is limited and inconsistent literature examining the relationship between food worry and mental health in the context of the COVID-19 pandemic. This study examined the association between food worry and mental health among community dwelling Canadian adults during the COVID-19 pandemic. METHODS: Adults age 16 years and older completed an anonymous online questionnaire between April 1, 2020 and November 30 2020. Measures of pre-pandemic and current food worry, depression (PHQ-2), anxiety (GAD-2), and sociodemographic variables were included. Multivariable logistic regression models were used to determine the association between food worry and symptoms of depression and anxiety. RESULTS: In total, 1605 participants were included in analyses. Worry about affording food was reported by 320 (14.78%) participants. In models adjusting for sociodemographic covariates, compared with people without food worry, participants who had food worry were 2.07 times more likely to report anxiety symptoms (aOR 2.07, 95% CI: 1.43 - 2.98, p < .001) and were 1.9 times more likely to report depressive symptoms (aOR 1.89, 95% CI: 1.39-2.57, p < .0001). Lower income, lower education, and pre-existing mental health conditions were significant predictors of symptoms of depression. Female gender, younger age, lower education, lower income, and pre-existing mental health condition were significant predictors of anxiety symptoms. CONCLUSION: Our study highlights the relationship between food worry and poor mental health. Policy supports such as improved income supports, clinical implications such as screening for food worry in primary care, referral to emergency food programs and support with meal planning may help mitigate mental health symptoms during the current pandemic, during future societal recovery from this pandemic and during future pandemics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".