Characterizing Trends in the Use of Food Donations and Other Food-Related Community-Based Social Assistance Programs in a Cohort of New Food Bank Users in Quebec, Canada
Bibliographic record
Abstract
Objective:To characterize 12-month trends in the use of food donations and other food-related community-based social assistance programs (CB-SAPs) during the first year following the enrollment of new food bank (FB) users in Quebec, Canada. Methods:A cohort of 1,001 newly registered FB-users in Quebec from the Pathways Study were followed-up during 12-month following baseline assessment. Outcomes were monthly use of food donations and other food-related CB-SAPs. Main predictors were alternative food source utilization (AFSU) profiles: 1) exclusive-FB-users; 2) FB+fruit/vegetable-market-users; and 3) Multiple/diverse-AFS-users. Covariates included sociodemographic characteristics, health status, and major life events. We fit Bayesian hierarchical mixed-effect models, accounting for spatial clustering, temporal correlation, and censoring. Results:We observed an overall downward trend of food donation use among study completers (n= 745). Each AFSU profile had a distinctive monthly trend of food donation use, but probabilities of use across the three profiles overlapped, between 44% and 55%. The use of other food-related CB-SAPs was low and not correlated with AFSU profiles. Conclusion:De novoFB-users use food donations in different ways over time according to specific contextual AFSU profiles.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".