Clarifying misconceptions about compassionate care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it