Assessing the caregiving burden of the family members of patients receiving palliative care in a tertiary care setting in Karachi, Pakistan: A mixed method approach
Bibliographic record
Abstract
Background: A family caregiver is a person from a patient’s family who is looking after the sick individual and helps to fulfill the needs related to activities of daily living and overall well-being of the patient. This may impact the life of the family caregiver. This study aimed to assess the caregiving burden of the family members of patients receiving palliative care. Methods: A convergent parallel mixed methodology design was employed to achieve the study objectives. Quantitative data were collected from 323 family caregivers using the Zarit Burden Interview (ZBI) tool. Participants from the quantitative phase were also invited to participate in individual qualitative interviews, which continued until data saturation was achieved. A semi-structured interview guide, including probing questions, was used to explore the burden experienced by family caregivers (n = 15). Results: The overall mean burden score of 29.22 depicted a high burden in the study cohort. The domains of burden in relationship and emotional well-being were mainly affected by the caregiving task. Three themes were extracted from the interview transcript analysis: care providers’ burden, factors that add to their burden, and factors that relieve the burden. Conclusion: Family caregivers of terminally ill patients receiving palliative care are highly burdened and stressed, impacting their own physical and emotional wellbeing. The impact ultimately affects the sick individual who needs care. Interventions, focusing on the caregiver’s physical and psychological concerns, that offer support in lessening the caregivers’ burden and improving patient care-related outcomes are of utmost importance. Keywords: care providers burden, oncology nursing, palliative care, low- and middle-income countries (LMICs)
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".