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Record W2232639118 · doi:10.5539/ass.v12n2p107

The Effectiveness of Teaching Stress Management on Self-Efficacy and Quality of Life in Divorcées

2016· article· en· W2232639118 on OpenAlexvenueno aff
Parvin Ehteshamzadeh, Zahra Dashtbozorgi, Rezvan Homaii, Zahra Zarei, Laleh Hamid

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStress managementPsychologyTest (biology)Quality of life (healthcare)Simple random sampleClinical psychologyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

This research aims to study the influence of stress management training in self-efficacy and quality of life of the divorcées in Ahvaz. The research sample consists of 15 divorcées in a control group and 15 in an experimental group, selected by simple random sampling. The self-efficacy Rolandick and Life Quality SF_36 Questionnaires were used in this research. The research project was of pre-test and post-test type with control group. Pre-test was administered for both groups and then stress management teaching as the independent variable was administered on experimental group, after completion of, post-test was administered on both groups. The MANCOVA was used to analyze data. The results showed that compared to the control group, stress management training increased self-efficacy and quality of life beliefs of divorcées in the experimental group.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 head0.025
GPT teacher head0.386
Teacher spread0.361 · 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
Published2016
Admission routes1
Has abstractyes

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