The nurse in assessing the burden of elderly caregivers
Bibliographic record
Abstract
The present study focuses on assessing the physical and emotional burden experienced by caregivers of elderly individuals. Its objectives were to evaluate caregiver burden using the Zarit Burden Interview (ZBI) and to plan nursing care strategies aimed at supporting elderly caregivers. This is a quantitative, descriptive, and exploratory study conducted in a reference institution for the treatment of elderly patients with some form of dementia, located in the city of Volta Redonda, Brazil. Inclusion criteria comprised informal caregivers, whereas formal caregivers were excluded from the study. Data analysis was performed according to the Evidence-Based Nursing Practice framework. The results showed that 53% of respondents reported never feeling burdened when caring for the elderly, 18% sometimes, 13% always, 11% frequently, and only 5% rarely. During interviews, caregivers expressed how caregiving affected their social lives: in 50% of cases, caregivers reported no social impact; however, when combining the other categories (sometimes, frequently, always), the remaining 50% indicated some level of social restriction, such as refraining from meeting friends, participating in family gatherings, or traveling. Therefore, nursing interventions should be implemented in a humanized manner to make the coexistence with elderly individuals more pleasant and less stressful. It is concluded that nurses can play a significant role in minimizing the physical and emotional burden of caregivers, thereby improving their overall quality of life.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".