Bibliographic record
Abstract
This study aims to identify the effectiveness of malnutrition intervention programs within senior populations. Government subsidized nutrition intervention programs, such as Meals on Wheels, play a vital role in the prevention of malnutrition in lower socioeconomic senior populations in the United States (Roy, 2006). For many older adults, meals received via nutrition programs serve as a lifeline, meeting essential nutritional needs and preventing premature institutionalized care (Lepore, 2019). Sixty-three Meals on Wheels (MOW) participants residing in Southern California were assessed, comparing nutritional status upon program intake against nutritional status after three to six months to identify improvement or decline. This study relied on self-reporting on the part of senior participants to explore the characteristics related to socioeconomic status and nutritional risk, and collect quantitative data. Further, it aimed to highlight whether nutritional risk was decreased through program usage. Access to the MOW nutrition program was found to correlate with a reduction in malnutrition risk among the participants in the study. Through the use of nutrition programs and their evaluations, malnutrition and malnutrition risk may be detected earlier, and subsequent measures for prevention can be employed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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".