The Relationship between Cancer Caregiver Burden and Psychological Outcomes: The Moderating Role of Social Connectedness
Bibliographic record
Abstract
The present study: (a) examined the extent of caregiver burden and psychological wellbeing and (b) tested whether social connectedness moderated the association between caregiver burden and psychological symptoms in caregivers of people with cancer. The cross-sectional survey study included 189 cancer caregivers (mean age = 36.19 years, standard deviation = 11.78; 80.4% female). Data were collected on caregiver burden, social connectedness, and depression and anxiety. Moderation analysis was conducted to examine the effect of social connectedness on the relationship between caregiver burden and depression and anxiety. Caregiver burden was positively associated with depression and anxiety symptoms. Controlling for significant demographic and caregiver characteristics, the moderation model showed as perceived social connectedness increased, the relationship between caregiver burden and depression decreased (β = −0.007, se = 0.004, 95% CI: −0.014, 0.000, p = 0.05). By contrast, social connectedness did not moderate the association between caregiver burden and anxiety. Findings have implications for the management of depression in cancer caregivers. Social connectedness appears to provide a protective buffer from the negative impacts of caregiving, providing increased psychological resources to manage the burden associated with caregiving, resulting in lower depression. Research on strategies to improve caregiver wellbeing through enhancing engagement with social networks in ways that improve perceived sense of connectedness with others is warranted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".