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Socio-Psychological Impact of Indices of Spousal Incompatibility on Marital Stability Among Couples in Lagos Metropolis, Nigeria

2013· article· en· W1831849508 on OpenAlexvenueno aff
Monday Bassey Ubangha, Bola O. Makinde, Rasheed Ajani Idowu, Ebele Aisha Raji

Bibliographic record

VenueCanadian social science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsCluster samplingMarital statusPsychologyPopulationSurvey researchMultistage samplingPearson product-moment correlation coefficientSocial psychologySociologyDemographyApplied psychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

The study investigated the effects of spousal incompatibility on marital stability in Lagos metropolis. In carrying out the research, a descriptive survey research design was employed. Cluster sampling method was used to select a sample of 200 respondents from the population of all married women in Eti-Osa Local Government Area of Lagos State. Three research questions and hypotheses were formulated to guide the study. A researcher-designed questionnaire was the major instrument used in collecting the data which were analyzed using Pearson Product Moment Correlation coefficient statistical tool. Results showed that there is a statistically significant positive relationship between the educational levels of spouses and marital stability. The findings also suggest that age differences between couples and religion also impacted on marital stability of couples. Based on these findings, it was recommended that youth who intend to marry should seek the informed counsel of a professional marriage counselor to help them in making the right choices and exposing them to the indices of spousal compatibility in marriage.

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)0.002
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.052
GPT teacher head0.371
Teacher spread0.319 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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