Falling Through the Social Safety Net: Food Stamp Use and Nonuse Among Older Impoverished Americans
Bibliographic record
Abstract
PURPOSE: Older adults are less likely than any other age group to use the federal Food Stamp Program. The personal and social costs of elderly diet insufficiency include disease exacerbation, depression, and increased hospitalization. In order to improve targeting and outreach efforts, this study identifies the characteristics of eligible older Americans who are not receiving food stamps and assesses the validity of the Andersen behavioral model for predicting impoverished older adults food stamp use. DESIGN AND METHODS: We conducted a secondary analysis of the 2003 American Community Survey, which is a nationally representative survey with a response rate of 96.7%. We restricted our study subsample to the 14,724 impoverished American citizens who were aged 65 years and older. We used bivariate and logistic regression analyses to compare the 2,796 food stamp recipients with the 11,928 nonrecipients. RESULTS: One in five impoverished older American citizens had received foods stamps in the preceding year. Female respondents, renters, younger respondents, disabled individuals, and those who received Supplemental Security Income or welfare were more likely to receive food stamps. The pseudo-R-square value indicated that the Andersen Behavioral Model explained 28% of the model's variability. IMPLICATIONS: Improved targeting is needed to enhance older adults' participation rates. Mobile and satellite food stamp offices in lower income neighborhoods and other innovative outreach programs that collaborate with community partners could also improve access. With the vast majority of impoverished older adults not receiving food stamps, strategies such as these are extremely important to rectify this situation among the most vulnerable group of older Americans.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.008 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".