Exposure to mass media family planning messages among men in Nigeria: analysis of the Demographic and Health Survey data
Bibliographic record
Abstract
Background: Family planning (FP) is essential for improving health and achieving reproductive goals. Although men are important participants in FP decision-making within households in Nigeria, a country with one of the highest rates of maternal mortality, we know very little about their exposure to mass media FP messages. Methods: = 13,294), and applying logistic regression analysis, we explored the factors associated with men's exposure to mass media FP messages in Nigeria. Results: A range of socioeconomic, locational, and demographic factors were associated with men's exposure to mass media FP messages. For example, wealthier, more educated, and employed men were more likely to be exposed to mass media FP messages than their poorer, less educated, and unemployed counterparts. In addition, compared to those in rural areas and other regions, men in urban areas as well as South East Region, were more likely to be exposed to mass media FP messages. Finally, younger men and those who belong to the traditional religion were less likely to be exposed to mass media FP messages, compared to their older and Christian counterparts. Conclusions: Based on these findings, we discuss implications and recommendations for policymakers as well as directions for future research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".