Wet Your Whistle with Water (W3) to Improve Water Intake in Seniors’ Care
Bibliographic record
Abstract
Context: Dehydration is a concern amongst older adults residing in retirement (RH) and long-term care (LTC) homes. Objectives: a) work with home team members to develop effective hydration strategies; b) implement these strategies; c) determine the capacity of home team members to provide process evaluation data on implementation, d) determine if administrative data is helpful in tracking dehydration-related events, and e) determine if a short, online education module can improve the hydration knowledge and attitudes of team members providing care. Methods: Wet your Whistle with Water (W3) included: voluntary online education module for team members; hydration reminders; water stations in common areas; and bi-monthly recreation activities providing beverages. Hydration-related administrative data from 56 LTC residents were analyzed for pre-post comparison. Findings: 218 individuals participated in the education and significant improvements in attitudes and knowledge noted. The LTC home held six hydration recreation programs with an average of 31 attendees and 15 beverages provided. Hydration station fluid intake was low (<120 oz per week). Bowel medications decreased non-signifcantly post-implementation; changes in other administrative variables were non-significant. Limitations: W3 could not be fully implemented in the RH due to challenges with staffing and collecting administrative data. Team member compliance with refilling water jugs, COVID-19 restrictions, and outbreak status impacted usability of the hydration station. Implications: W3 strategies were feasible but require home buy-in and a champion for implementation. Strategies (e.g., reminders) should be tailored to the home and be able to withstand outbreaks. Targeted education can improve confidence, attitudes, and knowledge.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".