Transition Experiences of Caregivers of Older Adults With Dementia and Multiple Chronic Conditions: An Interpretive Description Study
Bibliographic record
Abstract
INTRODUCTION: Family caregivers provide most of the care for older persons living with dementia (PLWD) and multiple chronic conditions (MCCs) in the community. Caregivers experience transitions, such as changes to their health, roles, and responsibilities, during the process of caring. Transitions encompass a time when caregivers undergo stressful responses to change. However, we know little about the transition experiences of caregivers of persons living with both dementia and MCCs. OBJECTIVE: This qualitative study explored the transition experiences of caregivers of PLWD within the context of MCCs, from the perspective of both caregivers and practitioners. The research question was the following: What are the transition experiences of family caregivers in providing care to older PLWD and MCCs living in the community? METHODS: This study was conducted using an interpretive description approach. Semistructured interviews were conducted with 19 caregivers of older community-dwelling PLWD and MCCs and 7 health-care providers working with caregivers in Ontario, Canada. Concurrent data collection and inductive data analysis were used. RESULTS: Caregivers of older PLWD and MCCs experienced four key transitions: (a) taking on responsibility for managing multiple complex conditions, (b) my health is getting worse, (c) caregiving now defines my social life, and (d) expecting that things will change. Findings highlight how the coexistence of MCCs with dementia complicates caregiver transitions and the importance of social networks for facilitating transitions. CONCLUSION: The study provided insight on the transition experiences of caregivers of older PLWD and MCCs. MCCs increased the care load and further complicated caregivers' transition experiences. Health-care providers, such as nurses, can play important roles in supporting caregivers during these transitions and engage them as partners in care.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".