Compassion in Health Care: An Empirical Model
Bibliographic record
Abstract
CONTEXT: Compassion is frequently referenced as a hallmark of quality care by patients, health care providers, health care administrators, and policy makers. Despite its putative centrality, including its institution in recent health care reform, an empirical understanding based on the perspectives of patients, the recipients of compassion, is lacking-making compassion one of the most referenced yet poorly understood elements of quality care. OBJECTIVES: The objective of this study was to investigate palliative cancer patients' understanding and experiences of compassion to provide a critical perspective on the nature and importance of compassion. METHODS: This grounded theory study used semi-structured interviews to investigate how patients understand and experience compassion in clinical care. Using convenience and theoretical sampling, 53 advanced cancer inpatients were recruited over a seven-month period from a specialized palliative care unit and hospital-wide palliative care service within a Canadian urban setting. Data were analyzed by four members of the research team through the three stages of Straussian grounded theory. RESULTS: Qualitative analysis yielded seven categories, each containing distinct themes and subthemes. Together, they constitute components of the compassion model-the first empirically based clinical model of compassion. The model defines compassion as a virtuous response that seeks to address the suffering and needs of a person through relational understanding and action. CONCLUSION: The components of the compassion model provide insight into how patients understand and experience compassion, providing the necessary empirical foundation to develop future research, measures, training, and clinical care based on this vital feature of quality care.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".