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Record W2255772043 · doi:10.5539/mas.v10n4p70

The Lived Experiences of Iranian Women, Injured from Their Husbands’ Infidelity

2016· article· en· W2255772043 on OpenAlexvenueno aff
Maryam Fatehizade, Akram Rahimi, Zahra Yousefi

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsInterviewNonprobability samplingPsychologyQualitative researchPhenomenological methodPopulationLived experienceClinical psychologySocial psychologyMedicineSociologyPsychotherapistEnvironmental health

Abstract

fetched live from OpenAlex

The current study mainly aims to investigate the lived experiences of women who were injured from their husbands’ infidelity in city of Isfahan in Iran country. The study was a phenomenological research with a qualitative method. The study population included women of Isfahan, who were injured from their husbands’ infidelity. In order to choose samples, the purposive sampling was applied; and a total number of ten women were selected out of all who were injured from their husbands’ infidelity. Moreover, the study tools included a semi-structured interview, the questions of which were provided according to the data obtained from the investigation of texts and sources with the purpose of recognizing the lived experiences of women injured from their husbands’ infidelity. The process of interviewing the participants continued until the saturation of the category. In order to analyze the data, we applied qualitative analysis and primary and secondary coding and categorizing method. The study results indicated that they lived experiences of women who were injured from their husbands’ infidelity, included ten main sub categories which are placed in four levels: confronting with tension, needs, spirituality, cooperation and effective communication.

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 categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.999

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 studies0.0020.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.029
GPT teacher head0.272
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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