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Record W4200027992 · doi:10.47634/cjcp.v55i3.71052

The Influence of a Self-Compassion Training Program on Romantic Relationship Conflict: An Exploratory Multiple-Case Study

2021· article· en· W4200027992 on OpenAlexaffvenue
Brittany N. Budzan, K. Jessica Van Vliet

Bibliographic record

VenueCanadian Journal of Counselling and Psychotherapy · 2021
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyPsychological interventionSelf-compassionThematic analysisDistressIntervention (counseling)Perspective (graphical)CompassionPsychological resiliencePerspective-takingSocial psychologyEmpathyConstruct (python library)Exploratory researchClinical psychologyQualitative researchMindfulness

Abstract

fetched live from OpenAlex

Separation and divorce are common occurrences in the Western world. Given that a transition out of a marriage can increase psychological distress in the members of the couple as well as in their children, preventive interventions are crucial for avoiding serious ruptures and for increasing relationship strength and resilience. A potential option for clinicians is to use interventions designed to increase self-compassion. This multiple-case study explored the influence of a self-compassion intervention on conflict within romantic relationships. Three women completed a self-compassion training CD, six sets of online questions, and two semi-structured interviews. Thematic analysis was used to construct detailed accounts of each participant’s experience. Participants perceived that self-compassion helped them to de-escalate conflict, increase self-awareness and self-acceptance, and facilitate perspective taking. This study may help inform future relationship interventions.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.070
GPT teacher head0.389
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Counselling and PsychotherapySame topicAttachment and Relationship DynamicsFrench-language works237,207