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Record W2401488726 · doi:10.5539/ass.v12n6p232

Effectiveness of Group Schema Therapy in Reducing the Symptoms of Major Depression in a Sample of Women

2016· article· en· W2401488726 on OpenAlexvenueno aff
Roghayeh Hashemi, Kobra Darvishzadeh

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyClinical psychologyBeck Depression InventorySchema (genetic algorithms)Analysis of covarianceTreatment and control groupsMedicinePhysical therapyPsychiatryInternal medicineStatistics

Abstract

fetched live from OpenAlex

<p class="a"><span lang="EN-US">Treatment of the symptoms of major depression is one of the important issues in the treatment of psychological disorders. This study aims to investigate effectiveness of group schema therapy in reducing the symptoms of major depression in a sample of women in the Ahvaz City. This is a quasi-experimental with two control and treatment groups. To this end, 30 married women in Ahvaz were selected using the convenience sampling method and were included in two treatment and control groups of 15 persons. After pre-test for both groups, the experimental group received schema therapy in 10 sessions for one month; however, the control group received no training. Beck Depression Inventory that has an acceptable reliability and validity was used to assess depression. Finally, test scores were analyzed by analysis of covariance. The results showed that group schema therapy training was effective at the level of error P<0.0001 on reducing the symptoms of depression in the treatment group. Accordingly, it can be concluded that the group schema therapy training affects the mental health promotion. Therefore, the intervention can be effective in preventing mental injuries.</span></p>

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.328
Teacher spread0.313 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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