Exposure to family planning messages on social media and its association with maternal healthcare services in Mauritania
Bibliographic record
Abstract
BACKGROUND: Mauritania, a lower-middle-income country in Northwest Africa, has one of the highest maternal and infant mortality rates worldwide and struggles to ensure optimal use of maternal healthcare services. Raising health awareness through family planning messages can promote maternal healthcare use, potentially reducing preventable maternal and child mortalities. The objective of the study was to assess the potential impact of exposure to family planning messages through social media on the utilization of maternal healthcare services among Mauritanian women. METHODS: Data from the 2019-20 Mauritania Demographic and Health Survey (MDHS) on 7,640 women were analyzed. Multiple logistic regression models were applied to examine the associations between exposure to family planning messages through social media and maternal healthcare services, specifically the timing and adequacy of ANC visits, and facility-based childbirth. Adjusted odds ratios with 95% confidence intervals (CI) were estimated. RESULTS: The percentage of timely initiation and adequate use of ANC among the participants were 65.6% and 45.1%, respectively. Approximately 75.0% of the women reported giving birth to their last child at a healthcare facility. Exposure to family planning messages on social media was significantly associated with increased odds of receiving adequate antenatal care visits (OR = 1.38, 95% CI = 1.12,1.71) and giving birth in a health facility (OR = 1.83, 95% CI = 1.09,3.08), Other factors such as age, health insurance, wealth, and desired timing of the last child were also found to be important predictors of maternal healthcare. CONCLUSION: The findings suggest that exposure to family planning messages on social media is strongly associated with adequate antenatal care and health facility-based childbirth, but not with early timing of antenatal care. Comprehensive maternal healthcare policies should consider the role of social media in promoting family planning messages.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".