LONELINESS AMONG LONG-TERM SPOUSAL CAREGIVERS: A GENDER-BASED ANALYSIS USING THE CLSA
Bibliographic record
Abstract
Abstract Spousal caregivers tend to undertake the most care for their loved ones. As a result, spousal caregivers also experience worse caregiving outcomes, including loneliness, than other types of caregivers. This study used three waves of data from the Canadian Longitudinal Study on Aging (2011 to 2021), and longitudinal analyses with the Linear mixed model were performed to examine the loneliness (measured by UCLA 3-item loneliness scale) of spousal caregivers over time. A total of 1569 participants were identified as long-term spousal caregivers (849 male and 720 female). The results showed that female spousal caregivers reported both a higher level of loneliness at the beginning and a greater increase of loneliness over time than male spousal caregivers. Besides participants’ demographic, social-economic and health-related factors, caregiving hours, social participation, and social support are the key predictors of loneliness. At the same time, female spousal caregivers experience a steeper increase in caregiver hours, and a greater decrease in social participation and social support over time. These disparities in changes over time amplify the negative impacts of caregiving on female spousal caregivers. The findings reveal the greater caregiver burden taken by female spousal caregivers than male ones over time. The study supports future programs and services for female spousal caregivers to manage caregiving tasks better and maintain active social interaction to balance caregiving and social life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".