LONELINESS AMONG OLDER FAMILY CAREGIVERS: A STUDY OF CAREGIVING INTENSITY, TYPE AND LOCATION BASED ON THE CLSA
Bibliographic record
Abstract
Abstract Caring for family members during aging is a risk factor for loneliness among older caregivers (65 years and older). However, loneliness is less understood than other caregiving outcomes (e.g., burden, depression) in caregiving literature. This study aims to examine loneliness among older caregivers using the second wave of data from the Canadian Longitudinal Study on Aging (2015 to 2018). Based on 6603 older caregivers, linear regression was conducted to examine the relationship between loneliness and caregiving intensity, caregiver type and care location, and ANCOVA was performed to examine the intersection of caregiving intensity and caregiver type, as well as caregiving intensity and care location. A higher level of loneliness is significantly associated with spousal caregivers (vs. non-spousal family caregivers), higher caregiving intensity (vs. lower caregiving intensity), and caregiving to someone living in another household or healthcare institution (vs. in the same household). In addition, spousal caregivers with higher caregiving intensity, and older caregivers to someone in healthcare institutions with higher caregiving intensity are two main risk groups for greater loneliness. The findings contribute to a better understanding of the relationship between loneliness and caregiving situations among older caregivers. Remarkably, more service programs are needed to support older caregiver who support loved ones in healthcare institutions.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".