Impact of Caregiving Burden on the Mental Health of Caregivers: A Study of Individuals Caring for Chronically Ill Patients
Bibliographic record
Abstract
The present research aims to investigate the relationship between the caregiving burden and its impact on the mental health of caregivers of chronically sick patients. It was hypothesized that the caregiving burden would be positively associated with higher levels of depression, anxiety, and stress among caregivers of chronically sick patients; and caregiving burden would significantly predict the psychological distress (depression, anxiety, stress) in caregivers of chronically sick patients. A sample of 220 caregivers (n=220) was selected conveniently which comprised of 102 males and 118 females selected from the different hospitals. The study employed a correlational research design to collect data, using two standardized scales, the Depression, Anxiety, and Stress Scale-21 (DASS-21) and Zarit Burden Interview. The data were analyzed by using SPSS to investigate the relationship between the caregiving burden and the caregivers' mental health. The findings showed a significant relationship between caregiving burden and increased psychological distress. This implies that higher caregiving burden leads to higher psychological distress. The findings emphasize the importance of proper interventions and support facilities for caregivers. The study adds to the existing knowledge by addressing the impact of the caregiving burden. Additional research is needed to investigate the potential mediator or moderators of the association between the burden and the mental health outcomes, thereby improving our understanding of the intricacies of caregiving experiences.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".