Determinants of food insecurity among elderly people: findings from the Canadian Community Health Survey
Bibliographic record
Abstract
Abstract Food insecurity among elderly people is a major public health concern due to its association with several health conditions. Despite growing research and implementation of diverse income-based policy measures, food insecurity among elderly people remains a major policy issue in Canada. Additional research could inform food policy beyond strategies that target improving the financial resources of elderly people. Drawing data from the Canadian Community Health Survey (N = 24,930), we explored the correlates of food insecurity among older adults using negative log-log logistic regression techniques. Our findings show that certain categories of elderly people are more prone to food insecurity. These segments include seniors who are visible minorities (OR = 1.29, p < 0.01), live alone (OR = 1.13, p < 0.05), have a very weak sense of community belonging (OR = 1.40, p < 0.001), in poor physical health (OR = 1.20, p < 0.01), and those in lower age and income categories. These findings corroborate previous studies that demonstrate that food insecurity among elderly people is a complex phenomenon influenced by diverse socio-economic factors. In Canada, food security policies targeted at elderly people have largely prioritised poverty alleviation through income support programmes. While these programmes can improve the purchasing power of elderly people, they may not be sufficient in ensuring food security. There is a need to embrace and further investigate an integrated approach that pays attention to other contextual socio-economic dynamics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".