Clarifying misconceptions about compassionate care
Bibliographic record
Abstract
AIM: To discuss the meaning of compassionate care as it applies to staff, patients and families in health and social care settings, its application to practice and how organizational infrastructures affect the delivery of care. BACKGROUND: The term compassion has assumed headline status and inclusion in current health and social care policy. Clarity of what the term means in practice is needed and may help to promote delivery of compassionate care consistently across health and social care settings. DESIGN: Discussion paper. DATA SOURCES: This article draws on data from an action research programme (Leadership in Compassionate Care Programme, 2007-2011) that focused on embedding compassionate care into practice and education and related literature focused on compassionate person-centred care. A literature search was conducted and articles published in English relating to the terms compassionate, person-centred care between 1999-2011 were included. DISCUSSION: Perceptions of compassion, practising compassion and the infrastructure to support compassion are discussed. IMPLICATIONS FOR NURSING: It is anticipated that this discussion will prompt further debate, raise awareness and help to clarify the meaning of compassion in everyday practice with patients, relatives and staff, so that it can be more clearly named, valued and defended. CONCLUSION: This article challenges some of the beliefs and values that underpin the meaning of compassionate care and its application to practice. It brings greater clarity to the meaning of compassion, which could be used to form the basis of shared visions of caring, both strategic and operational, across organizations.
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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 | 0.153 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.012 | 0.098 |
| Scholarly communication | 0.023 | 0.037 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.017 | 0.042 |
| 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".