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Record W7108073955 · doi:10.22098/jrp.2023.11893.1149

The effectiveness of mindfulness on early maladaptive schemas of abandonment, defectiveness/shame and stubborn criteria in betrayed women

2023· article· en· W7108073955 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsMcGill University
Fundersnot available
KeywordsMindfulnessCognitionAffect (linguistics)Significant differenceQuality of Life ResearchFocus group

Abstract

fetched live from OpenAlex

In recent years, infidelity and its effects on the family system have become the focus of psychological research more than ever. The current research was conducted with the aim of investigating the effectiveness of mindfulness on early maladaptive schemas of abandonment, stubborn criteria, mistrust and defectiveness in the betrayed women in Karaj in 2021. The method of this study was quasi-experimental with pre-test and post-test design. The total number of women was 122 who had referred to Mehr Aria Psychology Clinic in Karaj. The pre-test, post-test, and follow-up were conducted on all participants, who were randomly placed in two groups: the experimental and control groups, each consisting of 30 people. We provided eight mindfulness-based cognitive therapy (MBCT) sessions for only the experimental group. Then, MANCOVA was conducted and the results showed that there was a significant difference between the mean of Early Maladaptive Schemas of Abandonment, stubborn criteria, mistrust and defectiveness in pre-test and post-test (p < 0.05). Also, there was a significant difference between the mean of Early Maladaptive Schemas of Abandonment, stubborn criteria, mistrust and defectiveness in follow-up and pre-test (p < 0.05).

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.169
GPT teacher head0.541
Teacher spread0.372 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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