Exploring health insurance and knowledge of the ovulatory cycle: evidence from Demographic and Health Surveys of 29 countries in Sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Unplanned pregnancy continues to be a major public health concern in Sub-Saharan Africa (SSA). Understanding the ovulatory cycle can help women avoid unplanned pregnancy. Though a wide range of factors for ovulatory cycle knowledge in SSA countries has not been well assessed, the influence of health insurance on ovulatory cycle knowledge is largely unknown. As a result, we set out to investigate the relationship between health insurance enrollment and knowledge of the ovulatory cycle among women of childbearing age. This study aims to investigate the relationship between health insurance enrollment and knowledge of the ovulatory cycle among women of childbearing age in sub-Saharan Africa (SSA). METHODS: Demographic and Health Surveys (DHSs) data from 29 SSA countries were analyzed. The association between health insurance and ovulatory cycle knowledge was investigated using bivariate and multivariate multilevel logistic regression models among 372,692 women of reproductive age (15-49). The findings were presented as adjusted odds ratios (AOR) with 95% confidence intervals (CI). A p-value of 0.05 was considered statistically significant. RESULTS: The pooled result shows that the prevalence of knowledge of ovulatory cycle in the studied 29 SSA countries was 25.5% (95% CI; 24.4%-26.6%). Findings suggest higher odds of ovulatory cycle knowledge among women covered by health insurance (AOR = 1.27, 95% CI; 1.02-1.57), with higher education (higher-AOR = 2.83, 95% CI; 1.95-4.09), from the richest wealth quintile (richest-AOR = 1.39, 95% CI; 1.04-1.87), and from female headed households (AOR = 1.16, 95% CI; 1.01-1.33) compared to women who had no formal education, were from the poorest wealth quintile and belonged to male headed households, respectively. We found lower odds of ovulatory cycle knowledge among women who had 2-4 parity history (AOR = 0.80, 95% CI; 0.65-0.99) compared to those with history of one parity. CONCLUSIONS: The findings indicate that the knowledge of the ovulatory cycle is lacking in SSA. Improving health insurance enrollment should be considered to increase ovulatory cycle knowledge as an approach to reduce the region's unplanned pregnancy rate. Strategies for improving opportunities that contribute to women's empowerment and autonomy as well as sexual and reproductive health approaches targeting women who are in poorest quintiles, not formally educated, belonging to male headed households, and having high parity should be considered.
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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 | 0.005 | 0.000 |
| 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".