Predictors of depressive symptoms and physical health in caregivers of individuals with schizophrenia
Bibliographic record
Abstract
This cross-sectional study examined relationships among factors influencing caregiver burden, depressive symptoms, and physical health in family caregivers of individuals with schizophrenia. Two hundred family caregivers of individuals with schizophrenia completed standardized questionnaires related to depressive symptoms, physical health, perceptions of burden, coping, and social support. The results revealed that 19.5% of family caregivers of individuals with schizophrenia experienced significant depressive symptoms and 65.5% perceived themselves in poor physical health. Burden, self-controlling coping strategies, and physical health status were all independently predictive of depressive symptoms. Two emotion-focused coping strategies (self-controlling and escape-avoidance) were independently predictive of caregiver burden. Only burden predicted physical health status. The findings suggest that health professionals who provide community care for those with schizophrenia need to consider the "unit of care" as the family rather than the individual. The health status of family caregivers should be routinely assessed. Individualized interventions to reduce family burden could include community-based health professionals as well as trained community volunteers, opportunities for social interaction, and improving self-care for all family members.
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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) | 0.000 | 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) | 0.001 | 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".