Quality of Dying and Death of Patients With Cancer in Hospice Care in Uganda
Bibliographic record
Abstract
PURPOSE Despite advances in palliative care in Uganda, there has been relatively little recent patient-centered research investigating end-of-life outcomes in this region. We assessed the quality of dying and death of patients with cancer in hospice care in Uganda. METHODS Bereaved caregivers of patients who received hospice care in Uganda and died 2-12 months earlier (N = 201) completed the Quality of Dying and Death Questionnaire, which includes 31 items and single-item ratings of overall quality of dying and moment of death, and the FAMCARE measure of family satisfaction with cancer care. RESULTS Caregivers reported low-intermediate overall quality of dying (mean [M] standard deviation [SD], 3.25 [2.98]) and overall quality of moment of death (M [SD], 3.59 [3.51]), with 47.0% of the ratings of these two outcomes in the poor range, but the mean family satisfaction with care was high (M [SD], 77.75 [10.26]). Most Quality of Dying and Death Questionnaire items (74.2%) were rated within the intermediate range. Items rated within the good range were religious-spiritual, interpersonal, and personal facets; two items within the poor range reflected physical functioning. Overall quality of dying was most strongly correlated with pain control (Spearman's rho [r s ] = 0.45, P < .001), and overall quality of moment of death with state of consciousness before death and being unafraid of dying (r s = 0.42, P < .001). The FAMCARE score was not correlated with overall quality of dying or moment of death ( P = .576-.813). Only one FAMCARE item, information on managing patient's pain, was correlated with overall quality of moment of death (r s = –0.19, P = .008). CONCLUSION End-of-life care in hospices in Uganda requires further improvement, particularly with regard to symptom control. Patient-centered data could bolster advocacy efforts to support quality improvement of palliative care in this and other countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".