Using Marketing Science to Understand Contraceptive Demand in High‐Fertility Niger
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it