Bibliographic record
Abstract
Empathy, or the ability to “feel” another person’s experience, evokes strong emotions and activates the neural pathways in the pain region of the brain. Compassion is empathy combined with purposeful action to relieve suffering and impacts the brain’s reward centres. What are the outward impacts of compassion? Compassion is human connection, reciprocity, feeling cared for and caring for another. It reduces stress and cortisol for the receiver and giver. It reduces suffering and impacts all areas of the Quadruple Aim. Yet nearly half of the population of America and 63% of providers believe that the health system is not compassionate.How do you build “compassion skills”? Being a compassionate clinician is not about knowledge, but the quality of communication and relational interactions. Many hold the belief that this ability is naturally acquired or inherent in medical practice, but this ability is technical, rooted in capabilities, intentional, and requires continuous practice and refinement. Healthcare practitioners are at a disadvantage: the rigours clinical learning and the perceived time pressures of practice take precedent, limiting the opportunity for refinement and practice of these interpersonal communication skills. Looking to bridge the gap on continuous professional development and learning from other sectors, the Royal College of Physicians and Surgeons of Canada has been considering approaches for practice improvement in compassion. To make compassion skills tangible, the presenters offered a “coaching skills” program for physicians. Early evidence is pointing towards the positive impacts of this type of communication skills training on the therapeutic alliance.
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.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".