A culture of compassion: How timeless principles of kindness and empathy become powerful tools for confronting today’s most pressing healthcare challenges
Bibliographic record
Abstract
The role of compassion in healthcare is receiving increased attention as emerging research demonstrates how compassionate patient care can improve health outcomes and reduce workplace stress and burnout. To date, proposals to encourage empathy, kindness, and compassion in healthcare have focused primarily on training individual care providers. This article argues that increasing the awareness and skills of individuals is necessary but insufficient. Compassionate care becomes an organizational norm only when health leaders create and nurture a "culture of compassion" that actively supports, develops, and recognizes the role of compassion in day-to-day management and practice. The article profiles four organizations that have adopted compassionate healthcare as an explicit organizational priority and implemented practical measures for building and sustaining a culture of compassion. Common principles and practices are identified. These organizations demonstrate how compassion can lead directly to improved outcomes of primary importance to healthcare organizations, including quality and safety, patient experience, employee and physician engagement, and financial performance. They show how compassion can be a powerful yet often underappreciated tool for helping organizations successfully manage current challenges.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".