Weight Loss and Weight Gain: Multi-Level Determinants Associated with Resident 3-Month Weight Change in Long-Term Care
Bibliographic record
Abstract
This study examined factors associated with weight change in 535 residents in 32 long term care homes where 3-month weight records were available. Trained researchers and standardized measures (e.g., nutrition status, food intake, home characteristics) were used to collect data; weight change was defined as ±2.5%. Just over 25% of the sample lost and 21% gained weight. Weight stability was compared to loss or gain. Weight loss was associated with being male, malnourished (MNA-SF or BMI <25), energy and protein intake and oral nutritional supplement use, while weight gain was associated with being female, and a physically (e.g., less noise) and socially supportive dining room. Weight stability was associated with better cognition. A high proportion of residents had a significant weight change in 3 months. Modifiable factors associated with weight stability or gain suggest focusing interventions that promote food intake and improve the mealtime environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".