Joint trajectories of cognitive functioning and challenging behavior for persons living with dementia in long-term care.
Bibliographic record
Abstract
The current study examines the longitudinal relationship between dementia-related challenging behaviors (e.g., vocal disruption, physical aggression, repetitive behaviors, and restlessness) and cognitive functioning in the long-term care (LTC) context. A multivariate latent growth curve model within the structural equation modeling (SEM) framework was applied to data collected from 16,804 older adults upon admission to LTC and every 3 months for a period of 2.5 years. Increases in challenging behaviors were characterized by a significant positive linear and negative quadratic trend (i.e., a subtle leveling off at later assessment times), whereas increases in cognitive impairment were characterized by a positive linear trend. On average, individuals who were more cognitively impaired upon entry into LTC and who exhibited a steeper increase in cognitive impairment also exhibited more challenging behaviors at entry into LTC and a steeper increase in challenging behaviors, respectively. At the within-person level, individuals demonstrating an increase in cognitive impairment at a specific occasion were also more likely to demonstrate an increase in challenging behaviors at that same occasion; however, the magnitude of these effects was very small, suggesting limited practical implications. This study provides novel empirical evidence about the coevolution of cognitive impairment and challenging behaviors, going beyond prior research that has been either cross-sectional in nature, examined longitudinal change in only 1 variable, or simply looked at linear trends without attempting to explore the possibility of nonlinear change. Most importantly, this longitudinal examination of persons with dementia living in LTC has implications for how challenging behaviors can be better managed and for how new strategies can be implemented to prevent challenging behaviors.
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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 | 0.009 |
| 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 | 0.002 |
| 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".