Development of a multi‐component intervention to promote sleep in older persons with dementia transitioning from hospital to home
Bibliographic record
Abstract
BACKGROUND: Hospitalised older persons with dementia are commonly discharged with intensified sleep disturbances. These disturbances can impede the recovery process. Nurses are well-positioned to assist persons with dementia and their family caregivers in managing sleep disturbances during the transition from hospital to home. OBJECTIVES: To describe the development of a multi-component intervention to promote sleep. METHODS: We applied three stages of the intervention mapping method to develop a non-pharmacological, multi-component sleep intervention. The first stage involved a review of the literature to generate an understanding of the determinants of sleep disturbances experienced by persons with dementia in hospital and home settings. The second stage consisted of a literature review to identify therapies for managing commonly reported determinants of sleep disturbances. The third stage entailed delineation of the intervention components. RESULTS: The most common determinants of sleep disturbances experienced by persons with dementia in hospital and home settings were: physiological changes associated with ageing, sleep environments non-conducive to sleep, limited exposure to light and engagement in physical activity, stress and sleep-related beliefs and behaviours. Therapies found effective included: light therapy, physical activity therapy, sleep hygiene, and stimulus control therapy. These therapies were integrated into a multi-component sleep intervention to be provided using the teach-back technique, during and following hospitalisation. DISCUSSION: Consistent with the principles of patient engagement, the multi-component sleep intervention will be evaluated for its acceptability and feasibility. IMPLICATIONS FOR PRACTICE: The intervention has potentials to improve sleep during the transition from hospital to home.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".