Mass Media Exposure and Safer Sex Negotiation among Women in Sexual Unions in Sub-Saharan Africa: Analysis of Demographic and Health Survey Data
Bibliographic record
Abstract
(1) Background: Improving sexual autonomy among women in sexual unions comes with various benefits, including the reduction of sexually transmitted and blood-borne infections. We examined the relationship between mass media exposure and safer sex negotiation among women in sub-Saharan Africa (SSA). (2) Methods: The study involved a cross-sectional analysis of Demographic and Health Survey (DHS) data of 29 sub-Saharan African countries. A total of 224,647 women aged 15–49 were included in our analyses. We examined the association between mass media exposure and safer sex negotiation using binary logistic regression analysis. The results are presented using a crude odds ratio (cOR) and adjusted odds ratio (aOR), with their respective confidence intervals (CIs). Statistical significance was set at p < 0.05. (3) Results: The overall prevalence of safer sex negotiation among women in sexual unions in SSA was 71.6% (71.4–71.8). Women exposed to mass media had higher odds of negotiating for safer sex compared with those who had no exposure (aOR = 1.94; 95% CI = 1.86–2.02), and this persisted after controlling for covariates (maternal age, wealth index, maternal educational level, partner’s age, partner’s educational level, sex of household head, religion, place of residence, and marital status) (aOR = 1.40; 95% CI = 1.35–1.46). The disaggregated results showed higher odds of safer sex negotiation among women exposed to mass media in all the individual countries, except Ghana, Comoros, Rwanda, and Namibia. (4) Conclusions: The findings could inform policies (e.g., transformative mass media educational seminars) and interventions (e.g., face-to-face counselling; small group sensitization sessions) in SSA on the crucial role of mass media in increasing safer sex practice among women in sexual unions. To accelerate progress towards the achievement of the Sustainable Development Goal five’s targets on empowering all women and safeguarding their reproductive rights, the study recommends that countries such as Ghana, Comoros, Rwanda, and Namibia need to intensify their efforts (e.g., regular sensitization campaigns) in increasing safer sex negotiation among women to counter power imbalances in sexual behaviour.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".