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Record W4388724545 · doi:10.21926/obm.icm.2304052

An Exploration of Compassion Focused Therapy for Grieving Individuals

2023· article· en· W4388724545 on OpenAlexaff
Darcy L. Harris

Bibliographic record

VenueOBM Integrative and Complementary Medicine · 2023
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsThe King's UniversityWestern University
Fundersnot available
KeywordsCompassionDiscernmentExperiential learningPsychologyGriefPsychotherapistConstruct (python library)Experiential avoidanceEpistemologyAnxietyPedagogyPhilosophyComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.198
GPT teacher head0.454
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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