The Implementation of an Innovative Hydration Monitoring App in Care Home Settings: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: In response to marked concern regarding inadequate fluid intake recording in care homes, an innovative mobile hydration app was collaboratively developed. "Hydr8" aimed to facilitate accurate recording and communication of residents' fluid intake and ultimately increase care quality and patient safety. OBJECTIVE: The aim of this study was to examine the implementation of Hydr8 in a sample of care homes in one area in England. METHODS: The principles of Realist Evaluation and Action research were drawn upon throughout the study. Overall, 5 care homes participated in this study, 3 interview-only sites and 2 case-study sites, where interviews and observations were conducted at 3 time-points. Furthermore, 28 staff members participated, including care staff, management, a registered nurse, and administrative staff. RESULTS: Findings suggest that Hydr8 benefits practice, enhancing the understanding of hydration and person-centered care and improving staff communication. However, technical glitches hindered the seamless embedding of Hydr8 into everyday practice, and enthusiasm for long-term use was dependent on the resolution of issues. In addition, Hydr8 heightened perceptions of personal accountability, and while managers viewed this as positive, some staff members were apprehensive. However, individuals were enthusiastic about the long-term use and potential of Hydr8. CONCLUSIONS: Utilizing the findings of this study to further develop and adapt Hydr8 indicates the long-term use of Hydr8 as promising. Although perceptions of Hydr8 were primarily positive, setbacks in its implementation and use created difficulties in normalizing the solution into everyday practice. This study highlights the need for education related to hydration practice and a change of infrastructure in care home settings to implement technical solutions and changes to care.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".