Addressing Food Insecurity and Overweight/Obesity in Hospitalized Low-Income Latino Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Food insecurity (FI), the limited or unreliable availability of safe and nutritious food, is a pressing public health concern affecting millions of U.S. citizens. Unfortunately, FI tends to impact those who are most vulnerable (e.g., low-income minorities) and potentially increases obesity risks, diet-sensitive disease risks (e.g., hypertension and type 2 diabetes), and hospital utilization. Low-income Latino patients may be particularly sensitive to adverse outcomes based on unaddressed socioeconomic needs. Nurses are in a prime position to assess and address FI in these patients. Our article will discuss how nurses can be advocates in combating FI in Latino patients with overweight/obesity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it