The Monthly Cycling of Food Insecurity in Latinas at Risk for Diabetes: Methods, Retention, and Sample Characteristics for a Microlongitudinal Design
Bibliographic record
Abstract
Background: Food insecurity (FI) is a risk factor for type 2 diabetes (T2D) that disproportionately affects Latinas. We conducted a microlongitudinal study to examine the relationship of monthly cycling of FI and diabetes risk factors. Objective: This study aimed to determine the quantitative methodology, recruitment and retention strategies, predictors of retention across time, and baseline sample demographics. Methods: Participants were adult Latinas living in Hartford, Connecticut who were recruited through a community agency, invited to participate if they were receiving Supplementary Nutrition Assistance Program (SNAP) benefits, screened positive for FI using the 2-item Hunger Vital Sign Screener, and had elevated risk factors for T2D using the American Diabetes Association risk factor test. Using a microlongitudinal design, we collected data twice per month for 3 months (week 2, which is a period of food budget adequacy; and week 4, which is a period of food budget inadequacy) to determine if the monthly cycling of FI was associated with near-term diabetes risk (fasting glucose, fructosamine, and glycosylated albumin) and long-term risk (BMI, waist circumference, and glycated hemoglobin) markers. We determined whether household food inventory, psychological distress, and binge eating mediated associations. We examined Health Action Process Approach model constructs. To assess the relationship between monthly cycling of FI with diabetes risk markers, we used repeated measures general linear mixed models. To assess the role of mediators, we performed a causal pathway analysis. Results: Participant enrollment was from April 1, 2021 to February 21, 2023. A total of 87 participants completed 420 assessments or a mean of 4.83 (SD 2.02) assessments. About half (47/87, 54%) of the sample self-identified as Puerto Rican, mean age was 35.1 (SD 5.8) years, with 17.1 (SD 11.6) years in the mainland United States. Just under half (41/87, 47.1%) spoke Spanish only, 69% (60/87) had no formal schooling, and 31% (27/87) had less than eighth grade education. Modal household size was 4 including 2 children; 44.8% (39/87) were not living with a partner. About half (47/87, 54%) were unemployed, 63.2% (55/87) reported a monthly income
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".