What matters most in end-of-life care: perceptions of seriously ill patients and their family members
Bibliographic record
Abstract
BACKGROUND: Initiatives to improve end-of-life care are hampered by our nascent understanding of what quality care means to patients and their families. The primary purpose of this study was to describe what seriously ill patients in hospital and their family members consider to be the key elements of quality end-of-life care. METHODS: After deriving a list of 28 elements related to quality end-of-life care from existing literature, focus groups with experts and interviews with patients, we administered a face-to-face questionnaire to older patients with advanced cancer and chronic end-stage medical disease and their family members in 5 hospitals across Canada to assess their perspectives on the importance. We compared differences in ratings across various subgroups of patients and family members. RESULTS: Of 569 eligible patients and 176 family members, 440 patients (77%) and 160 relations (91%) agreed to participate. The elements rated as "extremely important" most frequently by the patients were "To have trust and confidence in the doctors looking after you" (55.8% of respondents), "Not to be kept alive on life support when there is little hope for a meaningful recovery" (55.7%), "That information about your disease be communicated to you by your doctor in an honest manner" (44.1%) and "To complete things and prepare for life's end - life review, resolving conflicts, saying goodbye" (43.9%). Significant differences in ratings of importance between patient groups and between patients and their family members were found for many elements of care. INTERPRETATION: Seriously ill patients and family members have defined the importance of various elements related to quality end-of-life care. The most important elements related to trust in the treating physician, avoidance of unwanted life support, effective communication, continuity of care and life completion. Variation in the perception of what matters the most indicates the need for customized or individualized approaches to providing end-of-life care.
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 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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".