Utilization of modern family planning methods among women of reproductive age group in North-Central Nigeria; rural – urban comparison
Bibliographic record
Abstract
Introduction: Contraception is one of the most successful developmental interventions, unique in women empowerment and population control. Family planning (FP) reinforces the right to determining the number and spacing of children. Hence, we determine the utilization of modern FP methods among urban and rural dwellers. Material and Methods: This is a cross-sectional study of reproductive age women in Lafia, Nigeria. Ten primary healthcare centers (PHCs) each were selected from the urban and rural locations over 6 months using multi-staged sampling technique. Questionnaire was administered, and the data were analyzed using SPSS V 23. P < 0.05 was considered significant. Results: Most participants were aged between 20 and 29 years. The prevalence of using modern methods of FP was 17.3%. More than three quarters of urban women are using contraceptives compared with one quarter of rural women. There was a difference between those who ever used measures to delay pregnancy and their locations, P = 0.049. Women in the rural areas use the cycle beads, while those in the urban areas use the injectables. Need for more children was the most common reason for discontinuing FP, others are FP failure, absence of spouse, and fear of side effects. Age of the participants was found to be a good predictor of using modern FP method, P = 0.022. Conclusion: There is low prevalence of modern FP utilization in this study despite knowing where to access the services (PHCs). Three of four urban women are using FP compared with one out of four among rural women. The cycle beads and the injectables were the most common methods. Age is a major determinant of using FP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".