Balancing burden and bond: challenges and motivations of family caregivers of patients with end-stage liver disease in Northern Ghana—a qualitative inquiry
Bibliographic record
Abstract
OBJECTIVE: This study explored the burdens and motivations of family caregivers (FCs) for patients with end-stage liver disease (ESLD) in a tertiary hospital in Ghana. DESIGN: A qualitative exploratory, descriptive approach with a purposive sampling technique was adopted. Data were collected through face-to-face semi-structured interviews. The interviews were audio-recorded, transcribed verbatim and analysed using content analysis. SETTING: Participants were recruited from a tertiary hospital in the Northern Region of Ghana. PARTICIPANTS: 15 FCs aged between 18 and 50 years caring for patients with ESLD were recruited. RESULTS: The study's findings revealed that FCs of individuals with ESLD encountered considerable challenges, including sleep deprivation, physical exhaustion, family conflicts, financial difficulties and social restrictions. These difficulties contributed to a sense of being overwhelmed as caregivers strived to fulfil their duties. Family bonds, reciprocal relationships and religious obligations were the motivation/driving force for FCs caring for relatives with ESLD. CONCLUSION: Integrating palliative care services in tertiary health facilities will reduce the burdens FCs of patients with ESLD face. Relevant stakeholders in the health sector need to develop culturally sensitive interventions to support FCs caring for patients with ESLD in Ghana.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".