Eating Behaviors of Older Adults Participating in Government-Sponsored Programs with Different Demographic Backgrounds
Bibliographic record
Abstract
The purpose of this study was to determine the food behaviors of nutritionally high-risk seniors as a function of their racial background, gender, marital status, and education level. A total of 69 seniors were identified to be at high nutritional risk using the Nutrition Screening Initiative (NSI) checklist. A supplemental questionnaire (SQ) was created to examine the risk factors in relation to the participant's demographic background. Key results indicated that Asians practiced healthy food behaviors and women were more likely to eat alone (p?0.05). Married participants (90.9%) were most likely to consume 2 meals or more each day. College educated individuals practiced healthier eating, eating 5 servings or more of fruits and vegetables (p?0.01) and 2 or more servings of milk and milk products (p?0.01). These preliminary findings indicate that more studies should be conducted to focus on the demographic characteristics and food behaviors among older populations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".