Factors Associated With Resident Intake of Thickened Liquids in Long-Term Care
Bibliographic record
Abstract
Purpose: Hydration is essential for health; however, long-term care (LTC) residents consume less fluid than is recommended, which may contribute to dehydration. Residents who drink thickened liquids likely consume even less than peers. Therefore, the aims of this study were to (a) determine if LTC residents who drink thickened liquids consume less fluid compared to those consuming thin liquids and (b) determine factors associated with fluid intake of residents who drink thickened liquids. Method: Participants who drank thickened liquids ( n = 68) were compared with participants who drank thin liquids ( n = 68). Fluid intake, cognition, diet prescription, and mealtime challenges were compared between groups. A stepwise multiple regression model assessed variables associated with fluid intake of residents who consumed thickened fluids. Results: All participants consumed less than recommended fluid volumes. No statistically significant difference was found in fluid intake between groups; however, the group consuming thickened liquids drank less than those consuming thin liquids. The thickened liquid group was also more likely to eat a modified diet, had higher levels of cognitive impairment, and had more mealtime challenges requiring more assistance. As age increased, thickened liquid intake decreased across study participants. Conclusion: These results highlight the need for interventions in LTC to support fluid intake in this vulnerable population.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".