Social support, social participation, & depression among caregivers and non-caregivers in Canada: a population health perspective
Bibliographic record
Abstract
Objectives: The study used data from the Canadian Longitudinal Study on Aging to investigate the relationships among social support (measured as affectionate support, emotional/informational support, positive social interaction, tangible support), social participation and depression in caregivers and non-caregivers.Method: Hierarchical multiple regression was used to investigate relationships among social support, social participation, and depression. Analyses of variance were used to examine differences in the means of social support, social participation, and depression between the two participant groups.Results: Higher levels of affectionate support, positive social interaction, and social participation were associated with lower depression scores. Social participation was a significant mediator of the relationship between caregiver status and depression. Caregivers reported significantly higher levels of affectionate support, emotional/informational support, positive social interaction, and social participation than non-caregivers. There were no between-group difference in depression scores.Discussion: The study provides support for the beneficial role of social participation in preserving caregiver mental health. Results are discussed in the context of policy and practice implications for caregivers in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".