Individual, household, and community-level predictors of modern contraceptive use among married women in Cameroon: a multilevel analysis
Bibliographic record
Abstract
BACKGROUND: Unintended pregnancy remains a major public health and socio-economic problem in sub-Saharan African countries, including Cameroon. Modern contraceptive use can avert unintended pregnancy and its related problems. In Cameroon, the prevalence of modern contraceptive use is low. Therefore, this study investigated the individual/household and community-level predictors for modern contraceptive use among married women in Cameroon. METHODS: Data for this study were derived from the nationally representative 2018-2019 Cameroon Demographic and Health Survey. Analysis was done on 6080 married women in the reproductive age group (15-49 y) using Stata version 14 software. Pearson χ2 test and multilevel logistic regression analysis were conducted to examine the individual/household and community-level predictors of modern contraceptive use. Descriptive results were presented using frequencies and bar charts. Inferential results were presented using adjusted odds ratios (aORs) with 95% confidence intervals (CIs). RESULTS: The results show only 18.3% (95% CI 16.8 to 19.8) of married women in Cameroon use modern contraceptives. Women's age (45-49 y; aOR 0.22 [95% CI 0.12 to 0.39]), education level (secondary education; aOR 2.93 [95% CI 1.90 to 4.50]), occupation (skilled manual; aOR 1.46 [95% CI 1.01 to 2.11]), religion (Muslim; aOR 0.63 [95% CI 0.47 to 0.84]), wealth quintile (richest; aOR 2.22 [95% CI 1.35 to 3.64]) and parity (≥5; aOR 3.59 [95% CI 2.61 to 4.94]) were significant individual/household-level predictors. Region (East; aOR 3.63 [95% CI 1.97 to 6.68]) was identified as a community-level predictor. CONCLUSIONS: Modern contraceptive use among married women in Cameroon is low. Women's education and employment opportunities should be prioritized, as well as interventions for married women, ensuring equity in the utilization of modern contraceptives across regions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".