Longitudinal Trajectories of Stress and Positive Aspects of Dementia Caregiving: Findings From the IDEAL Programme
Bibliographic record
Abstract
OBJECTIVES: Understanding what influences changes over time in caregiver well-being is important for the development of effective support. This study explores differences in trajectories of caregiver stress and positive aspects of caregiving (PAC). METHODS: Caregivers of community-dwelling individuals with mild-to-moderate dementia at baseline from the IDEAL cohort were interviewed at baseline (n = 1,203), 12 months (n = 917), and 24 months (n = 699). Growth mixture models identified multiple growth trajectories of caregiver stress and PAC in the caregiver population. Associations between study measures and trajectory classes were examined using multinomial logistic regression and mixed-effects models. RESULTS: Mean stress scores increased over time. A 4-class solution was identified: a "high" stable class (8.3%) with high levels of stress, a "middle" class (46.1%) with slightly increasing levels of stress, a "low" class (39.5%) with initial low levels of stress which slightly increased over time, and a small "increasing" class (6.1%) where stress level started low but increased at a steeper rate. Mean PAC scores remained stable over time. A 5-class solution was identified: 3 stable classes ("high," 15.2%; "middle," 67.6%; "low" 9.3%), a small "increasing" (3.4%) class, and 1 "decreasing" class (4.5%). For stable classes, positive ratings on study measures tended to be associated with lower stress or higher PAC trajectories and vice versa. Those with "increasing" stress also had worsening trajectories of several study measures including depression, relationship quality, competence, and ability to cope. DISCUSSION: The findings highlight the importance of identifying caregivers at risk of increased stress and declining PAC and offering them targeted support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".