Grocery Delivery of Healthy Foods to Pregnant Young Women With Low Incomes: Feasibility and Acceptability Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Poor maternal diets increase the risk of excess gestational weight gain which can contribute to serious intergenerational morbidity for both the mother and infant. Pregnant young women with low incomes have disproportionately high rates of inadequate fruit and vegetable consumption as well as excess weight gains during pregnancy. OBJECTIVE: Our aim was to describe the feasibility and acceptability of Special Delivery, a longitudinal nutrition intervention that delivers healthy foods to pregnant youth (aged 14-24 years) with low incomes. METHODS: The Special Delivery pilot study, conducted in Michigan, enrolled pregnant young women with low incomes. Study participants were sent twice-monthly grocery deliveries consisting of US $35 worth of healthy foods, primarily fruits and vegetables. Between grocery deliveries, participants received daily SMS text message prompts to confirm receipt of delivery and document diet and weight. Program feasibility was assessed by the number of grocery orders placed, delivered, and confirmed by participants. Qualitative interviews and SMS text message data were used to determine acceptability by assessing participants' perspectives on grocery delivery, participants' perspectives on dietary impact of the program, and foods consumed by participants. RESULTS: A total of 27 participants were enrolled in the pilot study. The mean age was 20.3 years (SD 2.0), and 59.3% (16/27) were African American or Black. During the pilot, 263 deliveries were sent with 98.5% (259/263) successful deliveries and 89.4% (235/263) deliveries confirmed by participants. Participants reported that grocery delivery was convenient; that delivered foods were high quality; and that the program improved their diet, increased access to healthy foods, and promoted healthy habits during pregnancy. CONCLUSIONS: A grocery delivery-based weight gain and nutrition intervention is both feasible and acceptable among low-income pregnant youth. Grocery deliveries were successfully completed and participants were willing and able to receive grocery deliveries, eat the healthy foods that were delivered, and communicate via SMS text message with study coordinators. The Special Delivery program warrants further evaluation for efficacy in promoting healthy weight gain for low-income youth during pregnancy.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".