SOCIAL NETWORK AND CAREGIVING EXPERIENCE AMONG SPOUSE AND ADULT-CHILD DEMENTIA CAREGIVERS: A MEDIATION ANALYSIS
Bibliographic record
Abstract
Abstract Evidence on differences in social network and their roles in associations between types of family caregivers and caregiving experience is limited. Based on the stress process model (SPM), we aimed to explore the different levels and interactions of social network and caregiving experience among spouses and adult-child caregivers. A questionnaire-based survey was conducted on a total of 146 dementia family caregivers (78 adult-child, and 68 spouses) in China. Data collection comprised four sections: (a) care-related stressors, (b) caregivers’ context, (c) social network using Lubben social network scale, and caregiving experience using short-form Zarit burden interview, and nine-item positive aspects of caregiving (PAC) scale. Linear regression, mediation model analysis, and interactive analysis were performed to explore associations between variables. Spouses experienced lower social network (β= -0.294, P=0.008) and higher PAC (β= 0.234, P=0.003) than adult-child caregivers, whereas no significant difference was found in caregiver burden between two groups. Mediation analysis suggested that associations between caregiver type and caregiver burden were indirect-only mediation effects by social network(β=0.140, 95%CI=0.066 to 0.228). The mediation effect of social network on caregiver type and PAC was the suppressing effect, and the interaction effect was significant (P for interaction =0.025). A higher social network was associated with higher positive aspects of caregiving among the spouse subgroup (β= 0.341, P=0.003). Social networks mediate responses to caregiving experiences among different care provider types and are vital intervention targets, especially for spousal caregivers. Our results can serve as references for identifying caregivers for clinical intervention.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".