Home Food Delivery to Address Food Insecurity Following Hospital Discharge
Bibliographic record
Abstract
Nearly 1 in 5 families with children in the United States are food insecure. Hospitalization of a child can exacerbate food insecurity, both during the hospitalization and after discharge. Although some hospitals provide free or subsidized meals during hospitalization, few address food insecurity in the immediate posthospitalization period. To address this gap, we developed an innovative Inpatient Food Pharmacy program. This program offers families of hospitalized children experiencing food insecurity a choice of 1 week of prepared meals, 6 months of monthly produce delivery, or both, after discharge. Our goals were to assess program enrollment, understand family preferences, and evaluate the program's feasibility and acceptability. Among 120 eligible families, 71 (59%) enrolled. Fifty-five families (77%) chose both prepared meals and produce delivery, 13 (18%) chose prepared meals only, and 3 (4%) chose produce delivery only. The program successfully delivered 6972 prepared meals and 348 boxes of produce over 10 months. Follow-up calls reached 41 (58%) of enrolled families, all of whom reported that the program met their acute food needs. Feedback from families and resource navigators suggested the program was acceptable. We aim to advocate for sustainable funding for food delivery for children and families experiencing food insecurity at 3 levels (1) institutionally, through our hospital's community benefit spending, (2) statewide, through a proposed Medicaid Section 1115 waiver providing grocery delivery to Medicaid-insured pregnant and postpartum individuals and their families, and (3) federally, through the Special Supplemental Nutrition Program for Women, Infants, and Children and the Supplemental Nutrition Assistance Program.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".