Improving hydration of care home residents by addressing institutional barriers to fluid consumption – an improvement project.
Bibliographic record
Abstract
Background: Older people are at risk of dehydration due to a wide range of age related physiological changes. Additional conditions such as dementia or physical frailty may contribute to low fluid intakes and further predispose the older people to dehydration. Care home residents are more likely to be admitted to hospital with dehydration, but there are few recent studies that evaluated the amount of fluids that residents consume or the barriers to hydration that they experience. Little is also known about the care they receive and how this may influence their fluid intakes. Objectives: To assess current hydration care in care homes, identify barriers to drinking adequate amounts and develop strategies to optimise fluid intakes in the older care home residents. Method: This study was conducted in one care home in London, which provides care to a multi-ethnic population of residents. The exploratory phase used observations, focus groups and questionnaires to determine how drinks were provided and to explore attitudes of staff and residents towards hydration care. The intervention phase used Model for Improvement framework to identify and test strategies to improve hydration for the residents. Results: Observations revealed that most residents consumed less than the recommended minimum of 1500ml of fluids. Hydration was not seen as a priority, and this resulted in several barriers that prevented staff to provide sufficient fluids, and the residents to consume them. Interventions were designed to overcome these issues and included: increasing the number of drink opportunities, improving preference compliance and introducing a new drinking equipment. During the testing, most interventions resulted in the residents consuming more fluids, but sustaining these interventions was difficult. Barriers to sustainability included poor leadership and task-oriented work culture. Conclusions: This study demonstrated that fluid intakes in care home residents are suboptimal. This is mostly due to insufficient number of opportunities for the residents to obtain drinks as well as not receiving adequate assistance and preferred drinks. Interventions which target these barriers have a potential to increase fluid intakes. Care homes need to implement appropriate strategies, but this requires organisational commitment with support from senior managers and strong leadership at operational level.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".