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Record W3216587221 · doi:10.47672/ejhs.858

Determinants of Disparity in Desired Fertility among Married Women in Urban and Rural Areas of Southwest Nigeria.

2021· article· en· W3216587221 on OpenAlexaboutno aff
Kazeem Sunmola, Johnson Olaosebikan, Temitope Joshua Adeusi

Bibliographic record

VenueEuropean Journal of Health Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsResidenceDemographyRural areaFertilityBivariate analysisQuarter (Canadian coin)GeographyLogistic regressionSocioeconomicsMultistage samplingMedicinePopulationStatisticsSociologyMathematics

Abstract

fetched live from OpenAlex

Purpose: The study examined the determinants of disparity in desired fertility among married women in urban and rural centres in Southwest Nigeria. Methodology: The study adopted a mixed method research design. A total number of one thousand one hundred and eighty-seven (1,187) women (urban=713; rural=474) of reproductive ages (15-49) years were drawn from Southwest States in Nigeria using multi-stage sampling technique. Questionnaire method was used to gather data from the field. Three levels of data analysis were undertaken to achieve the study objectives. Frequency distribution of socio-demographic factors by place of residence was used at the univariate stage of analyses, chi-square test and binary logistic regression were used at the bivariate and multivariate levels of analysis. Findings: The results showed that more than three-quarter (79.4%) desired four children and below while more than one-fifth (20.6%) of the women desired 5 children and above. Higher percentage of women (84.8%) desired four children and below in rural area when compared with women in urban centres (75.7%). However, among those that desired 5 children and above higher proportion (24.3%) was found in the urban centres when compared with their counterpart in rural areas (15.2%). There is significant relationship (p<0.05) between desired number of children and education of women, husband’s education, religion, age of husband and birth interval urban areas while there is significant relationship between desired number of children and women and husbands’ education in rural areas. Further analysis showed that women’s education especially women with below secondary education had higher odds of desiring more children than those with post-secondary education (OR: 1.57; 95% C.I: 0.70-3.56). In addition, women whose husbands had no education, below secondary education and secondary education were less likely to desire more children in the urban areas than those with post-secondary education. In rural areas, there was significant relationship (p<0.05) between women whose husbands had no education, below secondary education and desired fertility. Women whose husbands had no education and those whose husbands had below secondary education were 16.94 and 2.93 more likely to desire more children than those in the reference category respectively. In addition, women who were Christian were more likely to desire more children in urban areas than their counterparts who were traditionalists. It was also discovered that women who spaced their births for twenty-four months and below were 0.51 times less likely to desire more children than their counterparts in the reference category (OR:0.51; 95%C.I 0.32-0.80). Recommendation: The study recommends that policy aimed at reducing the desired fertility in both urban and rural areas should be implemented with the hope that high fertility rate will be reduced to a manageable level.

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)0.002
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.296
Teacher spread0.270 · 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
Published2021
Admission routes1
Has abstractyes

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