Family caregivers’ administration of medications at the end-of-life in China: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Effective medication management is crucial for ensuring timely pain and symptom control at the end of life. Dying in pain is a major concern for patients, yet some find less effective pain control at home. Family caregivers (FCGs) play a vital role in managing pain and symptom control for dying patients. However, the experience of administering medications at home for terminal-stage patients has not been widely recognized or understood. Our study aimed to explore the experiences of FCGs in administering medications to adult dying patients. METHODS: We conducted a directed content analysis of data from 73 semi-structured interviews with FCGs across 19 Chinese provinces from 2021 to 2023. FCGs were recruited through the Voluntary Cooperative Network Research. We asked, "Could you recall the end-of-life care process for the patients?" We aligned the themes with the five issues identified by Wilson et al. (2018): administration, organizational skills, empowerment, relationships, and support. RESULTS: FCGs in China exhibit concerns about over-engagement and empowerment in medication administration, concealing medication information from the patient, and medication accessibility. FCGs faced significant challenges in accurately identifying and addressing pain and symptoms, determining appropriate dosages, accessing effective medications, and managing the emotional stress associated with potential medication errors. Financial burden, medication regulatory restrictions, geographical inequality, and travel restrictions during COVID impeded patients and FCGs from accessing medication. A culturally specific finding is the use of alternative medicine at the end of life. CONCLUSION: Our findings build upon Wilson et al.'s framework and extend their insights on empowerment, highlighting the need for policies to support home-based palliative care professionals in training FCGs for effective medication administration.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".