Relationship between women’s decision-making power over their own health care and use of modern contraception in the Democratic Republic of the Congo: a cross-sectional secondary data analysis
Bibliographic record
Abstract
BACKGROUND: In sub-Saharan Africa, the use of modern contraception (MC) is a critical intervention aimed at reducing mortality rates associated with unintended, high-risk pregnancies. However, among Congolese women aged 15-49, the prevalence of MC use is low. Research suggests that women's general participation in decision-making is important in increasing MC use. However, little is known about the specific role of women's decision-making power over their own health care and how it relates to MC use. Thus, this study aimed to investigate the relationship between women's decision-making power over their own health care and use of MC. METHODS: A cross-sectional secondary data analysis was conducted using the most recent data from the 2013-2014 Democratic Republic of the Congo (DRC) Demographic and Health Survey. Women who were considered in need of contraception based on their family planning preferences were included in the study population (N = 6422). Multivariate logistic regression was used to determine whether women's decision-making power over their own health care was associated with the use of MC. RESULTS: Only one in ten women reported using a modern method of contraception. Logistic regression showed that women who made decisions alone regarding their own health care were more likely to use MC than women who had no say in these decisions, even after controlling for important covariates (OR 1.48; 95% CI 1.00, 2.17). CONCLUSION: The results of this study lend further support that promoting women's autonomy and right to independently make decisions regarding their own health may be important in increasing the use of MC in the DRC. However, in order to effectively empower women to negotiate for the use of MC, qualitative research is needed to further assess the relationship between decision-making power and MC use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".