Using Marketing Science to Understand Contraceptive Demand in High‐Fertility Niger
Bibliographic record
Abstract
Global initiatives aim to add 120 million new family planning (FP) users by 2020; however supply-side interventions may be reaching the limits of their effectiveness in some settings. Our case study in Niger used demand analysis techniques from marketing science. We performed a representative survey (N = 2,004) on women's FP knowledge, attitudes, needs, and behaviors, then used latent class analysis to produce a segmentation of women based on their responses. We found that Nigerien women's demand for modern FP methods was low, with majorities aware of modern methods but much smaller proportions considering use, trying modern methods, or using one consistently. We identified five subgroups of women with distinct, internally coherent profiles regarding FP needs, attitudes, and usage patterns, who faced different barriers to adopting or using modern FP. Serving subgroups of women based on needs, values, and underlying beliefs may help more effectively drive a shift in FP behavior.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".