Married women’s autonomy and post-delivery modern contraceptive use in the Democratic Republic of Congo
Bibliographic record
Abstract
BACKGROUND: Although use of modern contraception is considered beneficial in lowering maternal and child mortality rates, the prevalence of contraceptive use remains low in the Democratic Republic of Congo. This study examined modern contraceptive use and its linkage to women's autonomy. METHODS: Data were drawn from the 2013-2014 Democratic Republic of Congo Demographic and Health Survey. We selected unsterilized and non-pregnant married women who have given birth in the last three years (N = 6680). Logistic regression models were fitted to explore the relationship between women's autonomy and modern contraceptive use. RESULTS: The study found that only 7.1% of married women who had delivered within three years used modern contraceptive methods. After controlling for socioeconomic and demographic factors, the association between women's autonomy and modern contraceptive use remained positively significant (OR = 1.16; 95% CI = 1.05, 1.29). CONCLUSION: The findings from this study indicate that it is not enough to provide women with educational and employment opportunities to increase the uptake of modern contraception, but also to enhance women's assertiveness to make their own decisions regardless of their partners' preferences within household settings. It is critical for government and other stakeholders to roll out programs aimed at reducing gender inequality and improving women's autonomy in decision-making about reproductive health.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".