Sex Preference, Religion and Ethnicity Roles in Fertility Among Women of Childbearing Age in Nigeria: Examining the Links Using Zero-Inflated Poisson Regression Model
Bibliographic record
Abstract
The study aimed at examining the independent and joint influence of three cultural factors; religion, sex preference (SP) and ethnicity on fertility in Nigeria. Cross-sectional population-based cluster design approach was used for the study. The investigated population group was women of reproductive age (n=19,348). Probability of bearing ≥5 children, refined Total Fertility Rate and mean fertility were used to assess fertility. Data were analyzed using demographic and Zero-Inflated Poisson models. Fertility indices were higher among the Hausa/Fulani ethnic group than Igbo and Yoruba and also among Muslim women than Christians. Interaction shows that the probability of bearing at least five children was highest among women who; have no SP, belong to Islamic religious denomination, and of Hausa/Fulani ethnic group. The fertility incident rate ratio (IRR) was higher among women with no SP than women who have SP and also higher among Hausa/Fulani than Yoruba but lower among Christians than Muslims. Fertility differentials persists by ethnicity, religion and SP after controlling for other important variables. Difference exists in fertility among religious, ethnic groups and by SP in Nigeria. Fertility reduction strategies should be intensified in Nigeria, but more attention should be given to Muslims and Hausa/Fulani women.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".