Compassionate collaborative care: an integrative review of quality indicators in end-of-life care
Bibliographic record
Abstract
BACKGROUND: Compassion and collaborative practice are individually associated with high quality healthcare. When combined in a compassionate collaborative care (CCC) practice framework, they are reported to improve health, strengthen care provision, and control health costs. Little is known about how to integrate and measure CCC, yet it is fundamentally applied in palliative and end-of-life care settings. This study aimed to identify quality indicators of CCC by systematically reviewing and synthesizing the current state of the palliative and end-of-life care literature. METHODS: An integrative review of the palliative and end-of-life care literature was conducted using Whittemore and Knafl's method. Donabedian's healthcare quality framework was applied in the data analysis phase to organize and display the data. The analysis involved an iterative process that applied a constant comparative method. RESULTS: The final literature sample included 25 articles. Patient and family-centered care emerged as a primary structure for CCC, with overarching values including empathy, sharing, respect, and partnership. The analysis revealed communication, shared decision-making, and goal setting as overarching processes for achieving CCC at end-of-life. Patient and family satisfaction, enhanced teamwork, decreased staff burnout, and organizational satisfaction are exemplars of outcomes that suggest high quality CCC. Specific quality indicators at the individual, team and organizational levels are reported with supporting exemplar data. CONCLUSIONS: CCC is inextricably linked to the inherent values, needs and expectations of patients, families and healthcare providers. Compassion and collaboration must be enacted and harmonized to fully operationalize and sustain patient and family-centered care in palliative and end-of-life practice settings. Towards that direction, the quality indicators that emerged from this integrative review provide a two-fold application in palliative and end-of-life care. First, to evaluate the existing structures, processes, and outcomes at the patient-family, provider, team, and organizational levels. Second, to guide the planning and implementation of team and organizational changes that improve the quality delivery of CCC.
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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 | 0.028 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.030 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".