Evaluation of the Staff Educational Components of the PROMOTE Program to Improve Resident Hydration
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Inadequate fluid intake is prevalent among older adults living in care settings and can lead to dehydration-related events such as falls and hospitalization. Staff knowledge and confidence using diverse strategies is needed to provide adequate hydration to residents. PROMOTE is a multicomponent intervention designed to support staff to increase resident fluid intake between meals. This study evaluated the educational components of PROMOTE. METHODS: = 13) reviewed all educational materials, evaluated their usefulness and feasibility, and were interviewed to identify how to improve the materials. RESULTS: The educational video improved knowledge (e.g., self-rating of knowledge pre-test median 8, standard error of the mean (SEM) 0.18; post-test median 9, SEM 0.13) and confidence. Participants intended to use PROMOTE strategies in their work with residents (1 [very likely] to 10 [very unlikely] median 2.0 SEM 0.27). Key informants rated the hydration of residents as an organizational priority (median 9.0 SEM 0.42) and all indicated that they would use the educational video in their future training. Less feasible educational components as rated by key informants included huddle discussions and email pushes. Posters were seen as feasible (54%) but only somewhat useful (77%). CONCLUSIONS: Brief educational videos can improve staff knowledge and confidence regarding providing adequate hydration to residents. Having several educational components that can be used with this video was viewed positively. Recommendations were made to improve the materials.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".