A longitudinal study of food insecurity among low income families in Toronto
Bibliographic record
Abstract
Most studies of food insecurity in Canada have been cross‐sectional, yielding little insight into the chronicity or dynamics of food insecurity. Objectives of this study were to examine the experiences of food insecurity among low income families over a two‐year period and identify factors that mitigate or exacerbate severity of food insecurity. In 2006, data on household food security, demographics, and resources were collected from 485 low income, tenant families in Toronto recruited by door‐to‐door sampling in high poverty neighbourhoods. One year later, 76% were re‐interviewed. Working with an analytic sample of 361, fixed and random effects regression models were run to examine factors associated with severity of food insecurity, using a continuous scale based on the Household Food Security Survey Module. Of 290 families who were food insecure at baseline, 86% remained food insecure at follow‐up and 32% experienced more severe insecurity. Lower income, receipt of welfare, lack of employment, less education and lone motherhood were related to more severe food insecurity. Loss of employment or transition onto welfare was associated with an increase in severity of food insecurity. While changes in source of income were associated with shifts in severity, most families remained food insecure, highlighting the static nature of poverty in this group and corresponding chronic food insecurity. Funded by CIHR.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".