Food Insecurity and coping strategies among Senior citizens in Lubbock, Texas.
Bibliographic record
Abstract
Objective This study measured food insecurity among seniors (≥50 years) in the Lubbock area and self‐reported coping strategies used during periods of food insecurity in Lubbock, Texas. Methods This was a cross sectional study conducted in seven senior service providers (4 senior centers, one senior live in community, one volunteer group and meals on wheels participants). The demography (education, ethnicity and income), food insecurity (validated household food insecurity access scale (HFIAS)) and the reported coping strategies for food sufficiency were assessed using open ended questions administered during a face‐to‐face interview in the centers/households. Results Among the seniors (N=186 seniors), 82 (44.1%) were males and the rest were females. About a third (62 (33.3%) were married, 63(33.9%) were widowed/widowed and the rest were either divorced, single or living with a partner. About a quarter (24.2%) were Hispanic/latino, 22.6% were black/African Americans, 49.5% were non‐Hispanic white and the rest were Asian/Alaskan white. Only 57 (30.6%) had college education and more. According to the HFIAS 20 (10.8 %) reported severe food insecurity. Some (42 (22.6%) reported their participation in the SNAP program, 30(16.1%) participate in the food bank program and 15 (8.1%) of them participate in both programs. Majority 149 (79.0%) mainly depended on their social security funds while the rest depended on part time jobs (1.9%), full time jobs (1.9%) or on bonds or loans. A quarter 47 (25.3%) of the participants knew other seniors who had food insecurity issues. Conclusion Senior years are characterized by reduced income due to retirement. A tenth of seniors experienced very severe food insecurity. Reported coping strategies included the participation in the SNAP or South plains food bank program. Support or Funding Information This work was supported with Prof. Oldewage‐Theron's Start‐up grants
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".