An Exploration of Compassion Focused Therapy for Grieving Individuals
Bibliographic record
Abstract
In the past several decades, new understandings about grief have emerged. In the same time frame, a substantial body of literature has explored the components of compassion and their potential application to various clinical contexts. Compassion evolved from caring motivation associated with the evolutionary challenges of reproduction that involved the necessary care for offspring. Grief also has an evolutionary background that is rooted in core aspects of attachment and the assumptive world construct. Compassion Focused Therapy (CFT) translates the concepts of compassion into a form of therapy, which has the potential to address grief in an experiential and non-pathologizing way. Foundational components of CFT include a model of emotion regulation, experiential practices that enhance compassion-based responses, and the cultivation of wisdom and discernment regarding the nature of suffering. These aspects of CFT provide a uniquely oriented way to support those who grieve losses of all types. Compassion training enables clinicians to cultivate wisdom and discernment to accompany their intention and motivation to relieve suffering, including the grief that follows significant losses.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".