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Record W4405454109 · doi:10.1002/casp.70030

Exploring the Impact of a No‐Cost, Self‐Directed Self‐Compassion Intervention in Promoting Mental Health, Resilience and Self‐Compassion Among Women in Violent and Non‐Violent Relationships

2024· article· en· W4405454109 on OpenAlexafffund
Cara A. Davidson, Katie J. Shillington, Jennifer D. Irwin, Tara Mantler

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsWilfrid Laurier UniversityWestern University
FundersCanadian Institutes of Health Research
KeywordsSelf-compassionDomestic violenceIntervention (counseling)Mental healthPsychologyClinical psychologyPsychological resilienceSuicide preventionPoison controlMedicinePsychiatrySocial psychologyMindfulnessMedical emergency

Abstract

fetched live from OpenAlex

ABSTRACT Limited access to social services often hinders women experiencing intimate partner violence (IPV) from seeking support. This mixed‐methods (survey‐ and interview‐based) study investigated the impact of a no‐cost, one‐month, self‐directed self‐compassion intervention on women experiencing IPV compared with women in non‐violent relationships. Among the 28 participants ( n = 15 non‐IPV, n = 13 IPV), significant improvements were noted in total self‐compassion scores ( F (2,52) = 6.126, p = 0.004, η 2 p = 0.18), and specific domains such as self‐kindness ( F (2,52) = 6.552, p = 0.003, η 2 p = 0.20) and over‐identification ( F (2,52) = 4.251, p = 0.020, η 2 p = 0.14) over time. Interview findings indicated that women perceived meaningful improvements in their mental health and resilience because of the intervention, with some women in violent relationships reporting that the intervention facilitated leaving the relationship. This intervention demonstrates strong potential as an accessible, effective health promotion intervention for women in violent relationships.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.091
GPT teacher head0.419
Teacher spread0.328 · 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.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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