Predictors of change in sleep disturbance in Canadian long-term care facilities: a longitudinal analysis based on interRAI assessments
Bibliographic record
Abstract
PURPOSE: Sleep disturbance is prevalent in long-term care facilities (LTCFs), yet there is limited understanding of individual factors predicting changes in sleep within these populations. Our objective was to determine predictors of sleep disturbance in LTCFs and investigate variation in prevalence across facilities in two Canadian provinces-New Brunswick and Saskatchewan. METHOD: This retrospective longitudinal cohort study used interRAI comprehensive health assessment data from 2016 to 2021, encompassing 21,394 older adults aged ≥ 65 years across 228 LTCFs. Generalised estimating equations were used to determine predictors of sleep disturbance, with separate models for new and resolved sleep disturbance. Funnel plots were employed to assess facility-level variation, with facilities exceeding the 99.8th percentile control limit identified as outliers. RESULTS: The overall prevalence of sleep disturbance was 21.7%, with rates ranging from 3 to 56% across facilities, and 8% of facilities showing outlying rates. Nine predictors were significantly associated with the onset of new sleep disturbance, including being a male, being newly admitted, cognitive impairment, pain, daytime sleeping, chronic obstructive pulmonary disease, coronary heart disease, antipsychotics use, and sedative-hypnotics use. Significant predictors of resolved sleep disturbance were stroke, polypharmacy, and being newly admitted. Conversely, lower odds of resolved sleep disturbance were observed among daytime sleepers and residents taking sedative-hypnotics. CONCLUSION: This study underscores the high prevalence and variation of sleep disturbance in LTCFs, highlighting potential modifiable risk factors for improvement. Further research is needed to explore the interplay of institutional, environmental, and individual factors to develop targeted interventions that enhance the quality of care in LTCFs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".