EXPLORING THE LONG-TERM CARE TRAJECTORIES OF PERSONS WITH DEMENTIA—A WESTERN CANADIAN STUDY
Bibliographic record
Abstract
While a considerable body of research has focused on experiences of care within particular settings (e.g., home care, nursing home care or hospital) and the predictors of entry into such forms of care, little research has been conducted to examine the longitudinal experiences of older persons as they navigate across the overall long-term care (LTC) continuum. This is especially true for older persons with dementia. The purpose of this research is to use latent transition analysis (LTA) to explore the most common service use trajectories over time, and their predictors among a sample of long-term care clients with evidence of dementia. This study draws on four years of administrative data for n=3541 individual clients aged 65+ with evidence of dementia who were clients of long-term care (LTC) in a populous health region in British Columbia, Canada between January 1, 2008 and July 31, 2012. Four latent groups were found to represent the most common experiences of these clients: continuous home care, intermittent home care, residential care and an absorbing mortality class. Multinomial logistic regression analysis highlighted the characteristics of persons with dementia that were most significant in predicting membership in each of these common pathways. Predictors such as: age, marital status, living alone, income, chronic conditions, ADL scores, falls, cognitive performance and behavioural issues were most significant in the first year of service, and became less significant thereafter. Our results reveal that differential patterns in service use over time depend heavily on where people start in the care system.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".