The Moderating Role of Perceived Community Belonging in the Association between Food Insecurity and Health and Well-Being
Bibliographic record
Abstract
While extensive research has established a link between food insecurity and adverse health and well-being outcomes, less attention has been given to factors that may moderate this relationship. This study examines whether a strong sense of community belonging can buffer the impact of food insecurity on mood and anxiety disorders, poor self-rated mental and general health, and low life satisfaction. Data were drawn from the 2017-2018 Canadian Community Health Survey, a nationally representative cross-sectional survey conducted by Statistics Canada (N = 94,790). Findings from logistic regression models indicate that individuals experiencing food insecurity are more likely to report adverse health and well-being outcomes. A strong sense of community belonging moderates this relationship, reducing the harmful impacts of food insecurity across all measured outcomes. Gender-stratified analyses reveal that this protective effect is particularly pronounced for mental health outcomes-including mood disorder, anxiety disorder, and self-rated mental health-among women. These findings underscore the importance of strengthening community connections as a protective factor, especially in supporting women's mental health in food-insecure settings. For social work practice, these results highlight the need to promote community engagement and implement gender-sensitive interventions to address the unique vulnerabilities associated with food insecurity.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".