Construct Validation and Test-Retest Reliability of the Seniors in the Community: Risk Evaluation for Eating and Nutrition Questionnaire
Bibliographic record
Abstract
BACKGROUND: We performed two studies. Study 1 was a construct validation of Seniors in the Community: Risk Evaluation for Eating and Nutrition (SCREEN), a 15-item questionnaire for assessing nutritional risk. In Study 2, we examined the test-retest reliability of SCREEN. METHODS: Study 1 was a cross-sectional study, and Study 2 was a cohort study. For Study 1, ten diverse community sites were used to recruit participants. A total of 128 older adults attended a clinic to provide medical and nutritional history and anthropometric measurements. A dietitian interviewed each participant. Dietitians used clinical judgment to rate the probability of nutritional risk from 1 (low risk) to 10 (high risk). Spearman's rho correlation and receiver operating characteristic curves were completed. An abbreviated SCREEN was developed through multiple linear regression analysis. In Study 2, SCREEN was randomly distributed to members of a seniors' recreation center where a self-selected sample (n = 124) completed two mailed SCREENs, 4 weeks apart. The test-retest reliability was estimated through paired correlations of total scores and individual items. RESULTS: In Study 1, total and abbreviated SCREEN scores were significantly associated with the dietitian nutritional risk rating (rho = -.47 and rho = -.60, respectively). Study 2 revealed that the test-retest reliability of SCREEN was adequate. CONCLUSIONS: SCREEN appears to be a valid and reliable tool for identifying community-dwelling older adults at risk for impaired nutritional states.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".