Making the Most of Mealtimes (M3): effect of eating occasions and other covariates on energy and protein intake among Canadian older adult residents in long‐term care
Bibliographic record
Abstract
BACKGROUND: Food intake varies among long-term care (LTC) residents and, as a result, some residents are at risk for protein-energy malnutrition and its consequences, such as sarcopenia. The present study aimed to determine whether eating occasions, as well as other factors that may vary with eating occasions (e.g. family/volunteer presence), were associated with energy and protein intake at meals and snacks. METHODS: The present study comprised a secondary analysis of the cross-sectional Making the Most of Mealtimes study, including 630 residents (median age 88.00 years, range 62-107 years; 197 males) from 32 Canadian LTC homes. An analysis of variance compared protein and energy intake at meals and snacks. Mixed repeated measures linear regression testing for meal and relevant covariates (e.g. family/volunteer presence) was also conducted. RESULTS: Energy and protein intake was significantly associated with eating occasions (F = 44.31, P < 0.001; F = 12.72, P < 0.001), with the greatest energy intake at breakfast, and the greatest protein intake at dinner. Regression analysis confirmed these findings when considering other factors. Covariates associated with higher intake included: being male (+79 kcal; +3.4 g protein), living on a dementia care unit (+39 kcal; +2.1 g protein) and family/volunteer presence at meals (+58 kcal; +2.5 g protein). Intake was lowest in the oldest age group (-59 kcal; -3.6 g protein) and for those sometimes requiring eating assistance (-36 kcal; -2.0 g protein). CONCLUSIONS: Energy and protein intake appears to be associated with eating occasions. Based on these exploratory findings, LTC homes may consider providing more protein-dense foods at breakfast. Protein and energy dense snacks could also be used more extensively to support intake.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".