A Brief Intervention for Malnutrition among Older Adults: Stepping Up Your Nutrition
Bibliographic record
Abstract
Despite a multitude of nutritional risk factors among older adults, there is a lack of community-based programs and activities that screen for malnutrition and address modifiable risk among this vulnerable population. Given the known association of protein and fluid consumption with fall-related risk among older adults and the high prevalence of falls among Americans age 65 years and older each year, a brief intervention was created. Stepping Up Your Nutrition (SUYN) is a 2.5 h workshop developed through a public/private partnership to motivate older adults to reduce their malnutrition risk. The purposes of this naturalistic workshop dissemination were to: (1) describe the SUYN brief intervention; (2) identify participant characteristics associated with malnutrition risk; and (3) identify participant characteristics associated with subsequent participation in Stepping On (SO), an evidence-based fall prevention program. Data were analyzed from 429 SUYN participants, of which 38% (n = 163) subsequently attended SO. As measured by the SCREEN II®, high and moderate malnutrition risk scores were reported among approximately 71% and 20% of SUYN participants, respectively. Of the SUYN participants with high malnutrition risk, a significantly larger proportion attended a subsequent SO workshop (79.1%) compared to SUYN participants who did not proceed to SO (65.8%) (χ2 = 8.73, p = 0.013). Findings suggest SUYN may help to identify malnutrition risk among community-dwelling older adults and link them to needed services like evidence-based programs. Efforts are needed to expand the delivery infrastructure of SUYN to reach more at-risk older adults.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".