Contextualizing family planning messages for the BornFyne-PNMS digital platform in Cameroon: a community-based approach
Bibliographic record
Abstract
BACKGROUND: Family planning (FP) is crucial for reducing maternal and infant mortality and morbidity, particularly through the prevention of unsafe abortions resulting from unwanted pregnancies. Despite Cameroon's commitment to increasing the adoption of modern FP strategies, rural and poor populations still exhibit low demand due to limited access to healthcare services. This study documents the approach in developing family planning messages for the BornFyne prenatal management system as a platform to improve family planning awareness and enhance uptake. METHOD: This is a mixed-methods study that employed the Health Belief Model (HBM). The study included a cross-sectional survey and focus group discussions in four districts of Cameroon. The survey explored household perspectives of FP and the use of mobile phone. Focus group discussions involved women, men, and community health workers to gain in-depth insights. Thematic analysis using themes from the HBM guided the analysis, focusing on perceived benefits, barriers, and cues to action. RESULTS: The survey included 3,288 responses. Thematic analysis of focus group discussions highlighted knowledge gaps and areas requiring additional information. Identified gaps informed the development of targeted FP messages aligned with BornFyne objectives and the Health Belief Model. Results revealed that most respondents recognized the benefits of FP but faced knowledge barriers related to side effects, cultural influences, and communication challenges between partners. Focus group discussions further highlighted the need for education targeting both men and women, dispelling misconceptions, and addressing adolescent and youths' ignorance. The study emphasized the importance of tailored messaging for specific demographic groups and culture. CONCLUSION: Developing effective FP intervention messages requires a nuanced understanding of community perspectives. The BornFyne-PNMS family planning feature, informed by the Health Belief Model, addresses knowledge gaps by delivering educational messages in local dialects via mobile phones. The study's findings underscore the importance of community-based approaches to contextualizing and developing FP content targeting specific populations to generate tailored messages to promote awareness, acceptance, and informed decision-making. The contextualized and validated messages are uploaded into the BornFyne-family planning feature.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".