Building organizational compassion among teams delivering end-of-life care in the intensive care unit: The 3 Wishes Project
Bibliographic record
Abstract
BACKGROUND: The 3 Wishes Project is a semistructured program that improves the quality of care for patients dying in the intensive care unit by eliciting and implementing wishes. This simple intervention honors the legacy of patients and eases family grief, forging human connections between family members and clinicians. AIM: To examine how the 3 Wishes Project enables collective patterns of compassion between patients, families, clinicians, and managerial leaders in the intensive care unit. DESIGN: Using a qualitative descriptive approach, interviews and focus groups were used to collect data from family members of dying patients, clinicians, and institutional leaders. Unconstrained directed qualitative content analysis was performed using Organizational Compassion as the analytic framework. SETTING/PARTICIPANTS: Four North American intensive care units, participants were 74 family members of dying patients, 72 frontline clinicians, and 20 managerial leaders. RESULTS: The policies and processes of the 3 Wishes Project exemplify organizational compassion by supporting individuals in the intensive care unit to collectively notice, feel, and respond to suffering. As an intervention that enables and empowers clinicians to engage in acts of kindness to enhance end-of-life care, the 3 Wishes Project is particularly well situated to encourage collective responses to suffering and promote compassion between patients, family members, and clinicians. CONCLUSIONS: Examining the 3 Wishes Project through the lens of organizational compassion reveals the potential of this program to cultivate the capacity for people to collectively notice, feel, and respond to suffering. Our data document multidirectional demonstrations of compassion between clinicians and family members, forging the type of human connections that may foster resilience.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".