The Impact of Depression on Quality of Life in Caregivers of Cancer Patients: A Moderated Mediation Model of Spousal Relationship and Caring Burden
Bibliographic record
Abstract
Family caregivers play an important role in managing and supporting cancer patients. Although depression in family caregivers is known to negatively affect caregiver health, the mechanism by which it affects caregivers is not clear. The purpose of this study was to explore the influence of depression on quality of life (QoL) in family caregivers of patients with cancer. Specifically, this study examined (1) whether caring burden mediates the relationship between depression and QoL, and (2) how this mediating effect varies depending on the caregiver's relationship with the patient. This study performed a secondary analysis on cross-sectional survey data. Ninety-three family caregivers of cancer patients were included in the study. Moderated mediation analyses were conducted using PROCESS macro with the regression bootstrapping method. The moderated mediation models and the indirect effect of caregiver depression on QoL through caring burden were significantly different depending on caregivers' relationships with patients (i.e., spousal or non-spousal). Specifically, the indirect effect of caregiver depression on QoL was greater for the patient's spouse than for other family caregivers. Healthcare providers should focus on identifying caregivers' depression and relationship with the patient and offer tailored support and intervention to mitigate the caring burden and improve the caregivers' QoL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".