Modeling the “who” and “how” of social influence in the adoption of health practices
Bibliographic record
Abstract
Family planning is heralded as one of the ten most significant contemporary public health achievements, yet it remains underutilized in countries, especially in Sub-Saharan Africa, that might most benefit from it. While substantial strides have been made to address supply-side barriers to modern contraceptive (MC) adoption in these regions, demand-side obstacles like personal or partner opposition are less understood. This study investigates the role of social influence in shaping MC demand in communities with low modern Contraceptive Prevalence Rates (mCPR). Using the Structured Influence Process (SIP) framework, we examine how an individual's social relations and exposure to persuasive messages, either in support of or opposition to MC use, jointly influence their decision to adopt or reject contraceptives. Using survey data from two different Kenyan communities, both exhibiting low mCPR but one relatively higher than the other, we observe that mere exposure to MC users or non-users during free-time interactions is insufficient to sway usage decisions. However, the combination of direct contact with contraceptive users and persuasive messages emerges as a potent force of influence. In the lower mCPR community, only a few types of persuasive messages are circulated, and they are all consistently influential in either encouraging or discouraging MC use. These messages primarily appeal to individuals’ desire to do what is “right” by emphasizing social validation and deference to trusted authorities, or their desire to do what is “liked” by reinforcing interpersonal bonds and reciprocal obligations. In the higher mCPR community, a broader range of persuasive messages effectively promote MC use; however, only those invoking social shame effectively discourage it. These findings highlight a crucial distinction between “prevalent vs. persuasive” messaging: While many persuasive messages may be prevalent (i.e., used often), only a subset are also persuasive. Recognizing which messages are merely pervasive versus those that are genuinely effective is vital for efficiently allocating resources to promote or counter MC use narratives. Leveraging research across network science and persuasion, this study contributes to a more comprehensive understanding of how social influence shapes contraceptive decision-making. • Empirically shows that network ties influence modern contraceptive (MC) use through explicit persuasive messages about MCs. • Exposure to MC users does not lead to MC adoption unless close friends and family actively discuss their MC use decisions. • In higher-prevalence communities, many persuasive message types circulate but only shame-based ones effectively deter MC use. • In lower-prevalence communities, fewer persuasive message types circulate but all effectively influence MC use and non-use.
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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 | 0.003 | 0.013 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".