Communication and Quality of Care on Palliative Care Units: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Clinician-patient communication is central in palliative care, but it has not been described qualitatively which specific elements of communication are important for high-quality palliative care, particularly in the inpatient setting. OBJECTIVE: Our aim was to identify elements of communication that are central to quality of care and satisfaction with care on palliative care units (PCUs), as described by inpatients, family caregivers, and health care providers. METHODS: Qualitative interviews with patients/caregivers and focus groups with staff were conducted on four PCUs. Semi-structured interviews and focus groups elicited thoughts about the characteristics of satisfaction with care and quality of care for PCU inpatients and their family caregivers. Data were analyzed using a grounded theory method with an inductive, constant comparison approach; themes were coded to saturation. RESULTS: There were 46 interviews and eight focus groups. Communication was the most prevalent theme regarding satisfaction and quality of care, with five subthemes describing elements important to patients, caregivers, and staff. These included: 1) building rapport with patients and families to build trust and kinship; 2) addressing expectations and explaining goals of care; 3) keeping patients and families informed about the patient's condition; 4) listening actively to validate patients' concerns and individual needs; and 5) providing a safe space for conversations about death and dying. CONCLUSIONS: Patients, family caregivers, and health care providers affirmed that communication is a central element of quality of care and family satisfaction on PCUs. The five subthemes identified may serve as a structure for education and for quality improvement tools in palliative care inpatient settings.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".