Exposure to mass media family planning messages among post-delivery women in Nigeria: testing the structural influence model of health communication
Bibliographic record
Abstract
OBJECTIVES: While media campaigns are documented to be useful for increasing the uptake of family planning, very little is known about the population prevalence and correlates of exposure to mass media family planning messages among post-delivery women in Nigeria. We aimed to address this void by exploring the underlying factors that explain disparities in exposure to mass media family planning messages among post-delivery women in Nigeria. METHODS: Our study was a secondary analysis of the Nigeria Demographic and Health Survey, a nationally representative dataset of men and women. Using logistic regression techniques and drawing on the structural influence model of health communication, we explored post-delivery women's (N = 13,889) exposure to mass media family planning messages in Nigeria. RESULTS: We found that 32% of post-delivery women were exposed to family planning messages on mass media in Nigeria. At the bivariate level, Muslim women were less likely to be exposed to mass media family planning messages compared with Christian women (odds ratio [OR] 0.39; 95% confidence interval [CI] 0.36, 0.41); however, the OR became positive once we controlled for structural determinants such as household wealth and education (OR 1.22; 95% CI 1.07, 1.40). In the multivariate analysis, we found that traditionalist women (OR 0.29; 95% CI 0.14, 0.58) and women from rural areas (OR 0.69; 95% CI 0.62, 0.76) were less likely to be exposed to such messages. Moreover, richer, better educated, and employed women were more likely to be exposed to mass media family planning messages compared with their poorer, less educated and unemployed counterparts. Similarly, living in the South West region was positively associated with higher odds of being exposed to such messages. CONCLUSION: Findings were largely consistent with the structural influence model of health communication, as highlighted by inequalities in exposure to mass media messages. Based on these findings, we provide several policy recommendations.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".