Complexities of Addressing Food Insecurity in an Urban Population
Bibliographic record
Abstract
There is an association between food insecurity, poor health outcomes, and increased health care spending. The Temple Food Insecurity Program was initiated to screen patients for food insecurity as part of the post Temple University Hospital discharge process. The community is economically challenged and food insecurity is a significant problem. Food insecure patients were identified and referred to community-based resources, with a 30-day follow-up call. Screening was successful in 3655 patients, 27% (n = 987) of whom reported food insecurity. Of these patients, 66% (n = 647) were already receiving benefits through the Supplemental Nutrition Assistance Program (SNAP), but were still food insecure. All patients with food insecurity were referred to one of 2 resources for help. Despite significant need, less than a quarter of patients connected with these resources. Qualitative data revealed that some patients did not remember the information provided to them, were overwhelmed with poor health or other social determinants of health, had competing priorities, did not perceive the need for food assistance; and experienced system barriers. Health literacy also was an issue. Health care systems addressing food insecurity should consider the high prevalence of food insecurity in impoverished regions, the reality that SNAP benefits may not alleviate food insecurity for many patients, and the need for individualized, custom care plans that address barriers and reflect patient priorities and capabilities. Engaging patients differently may be aided by additional communication from community food resources directly to patients who provide permission for this added service.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".