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Record W2793293623 · doi:10.18311/jeoh/2017/19837

The Relationship between Marital Satisfaction and Alexithymia

2017· article· en· W2793293623 on OpenAlexaboutno aff
Sudabeh Darjazini, Maryam Moradkhani

Bibliographic record

VenueJournal of Ecophysiology and Occupational Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicProblem Solving Skills Development
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaPsychologyFeelingToronto Alexithymia ScaleAnalysis of varianceClinical psychologyScale (ratio)Marital statusDemographySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Background and Purpose: Expressing feelings and emotions not only has an important effect on intimacy growth, but it is also an important factor in distinguishing satisfied couples from the dis-satisfied ones. Thus, the present study aims at investigating the relationship between marital satisfaction and feelings identification and expression among women. Methods: The present study was a descriptive-correlational study with a single-subject design conducted cross-sectionally. In the present study, as many as 325 married women participated (from four health medical centers that had the highest number of referrals). In the present study, ENRICH Marital Satisfaction Scale as well as 20-item Toronto Alexithymia Scale (TAS-20) was used. For determining the significance of the relationship existing between background variables, marital satisfaction, and alexithymia, the researchers applied independent t-test, Analysis of Variance (ANOVA), and the Pearson correlation coefficient were respectively used. Findings: With respect to the marital satisfaction and women’s alexithymia, the results of one-way analysis of variance indicated no significant statistical difference in women’s mean marital satisfaction; (F = 1.381, P = 0.249) and (F = 1.836, P = 0.140) in terms of different ages, (F = 0.481, P = 0.696) and (F = 2.309, P = 0.076) in terms of different ages of their spouses, (F = 0.423, P = 0.655) in terms of different durations of marital life, (F = 0.612, P = 0.608) and (F = 1.94, P = 0.122) in terms of different number of children, and (F = 0.61, P = 0.603) and (F = 0.557, P = 0.644) as for women who lived with either parents or relatives (either theirs or those of their husbands). Independent t-test indicated that the there is no significant statistical difference between marital satisfaction (t = 0.185, df = 321, and p = 0.853) and alexithymia (t = 0.776, df = 321, and p = 0.444) at different age groups. The results of one-way analysis of variance indicated that there is a significant statistical difference in the mean of marital satisfaction in the groups with different educational levels (F = 5.8, P = 0.003) and different economic statuses (F = 11.209, P = 0.003). Scheffe’s Test indicated that this statistical difference has to do with women who have a higher level of education than that of their husbands. Moreover, Scheffe’s Test indicated that this difference has also to do with women enjoying a proper economic status. The results of one-way analysis of variance indicated a significant statistical difference in women’s mean alexithymia in groups with different educational levels (F= 4.369, P = 0.013) and different economic statuses (F = 4.369, P = 0.005). Scheffe’s Test indicated that this statistical difference has to do with women who have a higher level of education than that of their husbands. Moreover, Scheffe’s Test indicated that this difference has also to do with women enjoying a proper economic status. Discussion and Conclusion: The findings of the present study indicate that if alexithymia is solved, one can expect improved marital satisfaction. Moreover, the different educational levels and economic statuses are likely to affect marital satisfaction and alexithymia.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0030.000

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 head0.128
GPT teacher head0.469
Teacher spread0.341 · 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".

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Citations4
Published2017
Admission routes1
Has abstractyes

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