The prevalence and risk factors of apathy among the Canadian long-term care residents with Alzheimer's disease and related dementias
Bibliographic record
Abstract
BACKGROUND: Apathy is a prevalent and debilitating neuropsychiatric symptom among persons living with Alzheimer's disease and related dementias, particularly those residing in long-term care facilities (LTCF). Despite its profound effects on the quality of life for both residents and their caregivers, apathy remains underrecognized and poorly understood in the context of dementia care. OBJECTIVE: To investigate the prevalence and biopsychosocial characteristics of apathy among newly admitted residents with dementia in Canadian LTCF using an Apathy Index derived from the interRAI Minimum Data Set (MDS) 2.0. METHODS: This cross-sectional study analyzed data from newly admitted residents with dementia from various LTCF (N = 97,789) across seven Canadian provinces between 2015 and 2019. Logistic regression analysis was performed to determine the relationship between apathy and multiple variables including sociodemographic and clinical variables. The biopsychosocial model of health was used to guide analysis. RESULTS: The prevalence rate of apathy among the Canadian long-term care residents with Alzheimer's disease and related dementias was 13.1%. Apathy was associated with various variables including male sex, pain, use of psychotropics, high Activity of Daily Living Self-Performance Hierarchy Scale scores, depression, aggression, severe cognitive impairment, and insomnia. Preferences for certain activities such as card games, art and craft, reading, music and exercise were inversely related to apathy while gardening was not. CONCLUSIONS: By shedding light on this complex phenomenon within a Canadian context, we recommend that targeted interventions and improved care strategies to enhance the well-being of persons living with dementia should be prioritized in LTCF.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".