Beyond Purchasing Power: The Association Between Sense of Community Belongingness and Food Insecurity Among Older Adults in Canada
Bibliographic record
Abstract
Food is a basic human need, yet a significant proportion of older Canadian adults are vulnerable to food insecurity. The health risks associated with aging make food insecurity among this subgroup a critical policy issue. In Canada, policy solutions to food insecurity are however skewed toward the provision of income support to vulnerable groups. While these income support programs are timely, little emphasis is placed on social factors such as sense of community belongingness. This is despite evidence that food insecurity is a socially mediated experience that goes beyond the ability to purchase food. Drawing data from the Canadian Community Health Survey (n = 24,546) and using negative log–log regression, we examined the association between sense of community belongingness and food insecurity among older adults. Findings show that older adults with a “very weak” (odds ratio [OR] = 1.40, p < .001) and “somewhat weak” (OR = 1.23, p < .01) sense of community belongingness were significantly more likely to be food insecure compared to those with a “very strong” sense of belongingness. This study contributes to a growing body of the literature that demonstrates the need for an integrated approach to addressing food insecurity – one that goes beyond income support to include consideration of social factors like sense of community belonging.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".