Healthcare Providers’ Knowledge of Disordered Sleep, Sleep Assessment Tools, and Nonpharmacological Sleep Interventions for Persons Living with Dementia: A National Survey
Bibliographic record
Abstract
A large proportion of persons with dementia will also experience disordered sleep. Disordered sleep in dementia is a common reason for institutionalization and affects cognition, fall risk, agitation, self-care ability, and overall health and quality of life. This report presents findings of a survey of healthcare providers' awareness of sleep issues, assessment practices, and nonpharmacological sleep interventions for persons with dementia. There were 1846 participants, with the majority being from nursing and rehabilitation. One-third worked in long-term care settings and one-third in acute care. Few reported working in the community. Findings revealed that participants understated the incidence of sleep deficiencies in persons with dementia and generally lacked awareness of the relationship between disordered sleep and dementia. Their knowledge of sleep assessment tools was limited to caregiver reports, self-reports, and sleep diaries, with few using standardized tools or other assessment methods. The relationship between disordered sleep and comorbid conditions was not well understood. The three most common nonpharmacological sleep interventions participants identified using were a regular bedtime routine, increased daytime activity, and restricted caffeine. Awareness of other evidence-based interventions was low. These findings will guide evidence-informed research to develop and test more targeted and contextualized sleep and dementia knowledge translation strategies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".