Community-based social assistance programs and household food insecurity among de novo food-aid seekers in Quebec, Canada
Bibliographic record
Abstract
Objective: To examine the association between the use of community-based social assistance programs (CB-SAPs) and the reduction of household food insecurity among de novo food-aid seekers in Quebec, Canada. Study design: Prospective Cohort Study. Methods: cohort study (2018-2020). The outcome was any reduction in the severity of Household Food Insecurity. Exposures included three CB-SAPS:1) using food donations, 2) using food-management related CB-SAPs (other than food donations), and 3) using CB-SAPs unrelated to food. We used Longitudinal Targeted Maximum Likelihood Estimation (LTMLE) to estimate the Relative Risk (RR) and LTMLE for working Marginal Structural Models to estimate Average Additive Treatment Effects (ATE) of the relationship between the use of CB-SAPs and Household Food Insecurity. Results: The use of CB-SAPs showed a trend towards reduction of Household Food Insecurity. Compared to households using exclusively food banks at baseline, households with multiple-food-acquisition (Multiple AFS) health-promoting practices were more likely to reduce (in the relative scale) Household Food Insecurity by using: food donations (RR: 1.30; 95 %CI:1.01, 1.60); food-management related CB-SAPs (RR: 1.28; 95 %CI:1.03, 1.58); and CB-SAPs unrelated to food (RR: 1.33; 95 %CI:1.03, 1.62). Multiple AFS showed a reduction in the Household Food Insecurity (absolute) scale, especially among food-management related CB-SAPs users (ATE: -0.24; 95 %CI: 0.43, -0.04). Conclusions: CB-SAPs use contributes to reducing Household Food Insecurity. This contribution varies depending on the food-acquisition health-promoting practices of food-aid seeker households.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".