The colours and contours of compassion: A systematic review of the perspectives of compassion among ethnically diverse patients and healthcare providers
Bibliographic record
Abstract
OBJECTIVE: To identify and describe the perspectives, experiences, importance, and impact of compassionate care among ethnically diverse population groups. METHODS: A systematic search of peer-reviewed research focused on compassionate care in ethnically diverse populations published between 1946 and 2017 was conducted. RESULTS: A total of 2296 abstracts were retrieved, out of which 23 articles met the inclusion criteria. Synthesis of the literature identified the perspectives, facilitators and barriers of compassion in healthcare within ethnic groups. Compassion was described as being comprised of healthcare provider (HCP) virtues (honesty, kindness, helpful, non-judgment) and actions (smile, touch, care, support, flexibility) aimed at relieving the suffering of patients. The importance and impact of providing compassion to ethnically diverse patients was also identified which included overcoming cultural differences, alleviating distress at end-of-life, promoting patient dignity and improving patient care. This review also identified the need for more contextual studies directly exploring the topic of compassion from the perspectives of individuals within diverse ethnic groups, rather than superimposing a pre-defined, enculturated and researcher-based definition of compassion. CONCLUSIONS: This review synthesizes the current evidence related to perceptions of compassion in healthcare among diverse ethnic groups and the role that compassion can play in bridging ethno-cultural differences and associated challenges, along with identifying gaps in literature related to compassionate care within diverse ethnic groups. Establishing an evidence base grounded in the direct accounts of members of diverse ethnic communities can enhance culturally sensitive compassionate care and improve compassion related health outcomes among diverse ethnic groups.
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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".