Wet Your Whistle with Water (W3) to Improve Water Intake in Seniors’ Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it