A pattern language of compassion in intensive care and palliative care contexts
Bibliographic record
Abstract
BACKGROUND: Compassion has been identified as important for therapeutic relationships in clinical medicine however there have been few empirical studies looking at how compassion is expressed different contexts. The purpose of this study was to explore how context impacts perceptions and expressions of compassion in the intensive care unit and in palliative care. METHODS: This was an inductive qualitative study that employed sensitizing concepts from activity theory, realist inquiry, phenomenology and autoethnography. Clinicians working in intensive care units and palliative care services wrote guided field notes on their observations and experiences of how suffering and compassion were expressed in these settings. Data were analyzed using constructivist grounded theory. RESULTS: Fifty-eight field notes were generated, along with transcripts from three focus groups. Clinicians conceptualized, observed, and expressed compassion in different ways within different contexts. Patterns of compassion identified were relational, dispositional, activity-focused, and situational. A pattern language of compassion in healthcare was developed based on these findings. CONCLUSIONS: Recognizing compassion as shifting patterns of diverse attitudes, behaviours, and relationships raises numerous questions as to how compassion can be developed, supported and recognized in different clinical settings.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".