Bibliographic record
Abstract
PURPOSE OF REVIEW: This article is an investigation of the possibility that compassion is not a discrete feature but an emergent and contingent process that is at its base enactive. Compassion must be primed through the cultivation of various factors. This article endeavors to identify interdependent components of compassion. This is particularly relevant for those in the end-of-life care professions, wherein compassion is an essential factor in the care of those suffering from a catastrophic illness or injury. The Halifax Model of Compassion is presented here as a new vision of compassion with particular relevance for the training of compassion in clinicians. RECENT FINDINGS: Compassion is generally valued as a prosocial mental quality. The factors that foster compassion are not well understood, and the essential components of compassion have not been sufficiently delineated. Neuroscience research on compassion has only recently begun, and there is little clinical research on the role of compassion in end-of-life care. SUMMARY: Compassion is in general seen as having two main components: the affective feeling of caring for one who is suffering and the motivation to relieve suffering. This definition of compassion might impose limitations and will, therefore, have consequences on how one trains compassion in clinicians and others. It is the author's premise that compassion is dispositionally enactive (the interactions between living organisms and their environments, i.e., the propensity toward perception-action in relation to one's surrounds), and it is a process that is contingent and emergent.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".