Relationships between income, food expenditures and purchasing choices in African American churchgoers in Baltimore City
Bibliographic record
Abstract
Healthy Bodies Healthy Souls, a church‐based diabetes prevention program, will be evaluated in a sample of 375 non‐diabetic adult respondents, using psychosocial (knowledge, self‐efficacy, intentions), behavioral (diet, physical activity) and health (BMI, blood pressure, waist circumference) measures. Analysis of the baseline data on the first 138 respondents showed that over 78% have a high school degree and 73% have household incomes more than $30,000. Using the Chi‐Squared test, an association was found between having an annual income greater than $30,000 and monthly grocery expenditures greater than $180 (p=0.004). Expenditures on single meals at carry out, fast food, and sit down restaurants did not vary significantly with household income. Respondents from households that grossed more than $30,000 annually were more likely to purchase 100% whole wheat bread at least once (p=0.03) and fresh fruit greater than three times (p=0.045) in the 30 days prior to responding to the questionnaire. However, no association was observed between the purchasing frequency of high fiber cereals, multigrain bread, brown rice, and cooking spray. These findings can help guide future programs aimed at influencing food purchasing decisions in African American communities. Grant Funding Source : American Diabetes Association
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".