Prevalence and determinants of contraceptive use among women in rural communities in Eastern region of Ghana
Bibliographic record
Abstract
Abstract Background Despite the importance of contraceptives in reducing unwanted pregnancy and other related maternal morbidity and mortality, contraceptive usage is very low among women in their reproductive ages. In Ghana, there is an increase in contraceptive use for traditional and modern methods in rural areas. However, there is a paucity of studies examining the factors associated with any contraceptive method in the rural Eastern region of Ghana. Therefore, this study seeks to examine the prevalence and determinants of current use of any contraceptive method among women of reproductive age in the rural Eastern Region of Ghana. Methods A community-based cross-sectional study was conducted among women of reproductive age in the rural Eastern region of Ghana. A structured questionnaire was used to interview rural women in Lower Manya and Upper Manya Krobo districts of Eastern region who were selected using a simple random sampling technique. The data were analysed using Stata version 16. A Binary logistic regression was used to examine the determinants of current use of any contraceptive use (traditional and modern methods). Results The prevalence of contraceptive use was 27.8%. From the unadjusted analyses, age (p = 0.001), marital status (p = 0.087), desire for another child (p = 0.089) and head of household (p = 0.013) were independently associated with contraceptive use. In the adjusted analysis, contraceptive use was significantly higher among respondents aged 18–35 years (aOR:3.27; 95%CI:1.46–7.34;p = 0.004) or 36–40 years (aOR:2.14;95%CI:0.97–4.71;p = 0.049), husbands/partners who were head of households (aOR:3.40; 95%CI: 0.96–12.10; p = 0.028) compared with those aged 41–49 years and respondents who were head of households respectively. Contraceptive use was significantly lower among migrants (aOR:0.59; 95%CI:0.31–1.09; p = 0.036) compared with non-migrant. Conclusion This study highlights the factors associated with contraceptive use and the need to improve campaigns and educate rural women on contraceptives to prevent unwanted pregnancy and space birth. Family planning programs should target young women, non-migrants and male-headed households to design an intervention to increase contraceptive use in rural areas.
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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 | 0.000 | 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".