Compassion Training: The Missing Link in Healthcare Education?
Bibliographic record
Abstract
Compassion is an essential skill in whole person care. But, can it be cultivated through training?Current research in neuroscience elucidates the mechanisms of empathy and compassion and provides a new framework for professional education. It suggests that clinical detachment is neither effective for ensuring good care, nor a realistic strategy to prevent burnout. Cultivating compassion on the other hand, increases non-judgmental awareness, builds resilience, and enables us to respond more effectively to others’ needs with greater empathy (Frickson, 2008, Klimecki, 2102, Lutz, 2004). Moreover, it is a skill we can learn (Wasner et al, 2005, Lutz 2009). Despite these findings, however, training in compassion is largely absent in current professional curricula.Presenters will review current findings on compassion and its benefits, and demonstrate how we can train in it using examples from two unique compassion skills-training curricula: (1) a training for pediatric residents working in an inner-city hospital and (2) a certificate program in contemplative end-of-life care for hospice/palliative care workers. These models are inspired by the contemplative tradition of Tibetan Buddhism, with its long-standing and effective methodologies for deepening the human capacity for compassion. This approach has formed the basis of many scientific studies on compassion and the emerging field of contemplative-based, secular training models (Lutz, 2009). Participants engage in contemplations on compassion as well as mindfulness and meditation. The aim is to support clinicians to generate self-compassion - the foundation for building resilience and extending compassionate care – thereby improving communication and the overall quality of care.The workshop will introduce key principles, feature hands-on experience of selected methods, and include a discussion of the potential impact on the greater healthcare system.A wider implementation of compassion training promises to be the missing link for building a fulfilling clinical practice and strengthening our capacity to provide effective whole-person 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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".