Induction of social contagion for diverse outcomes in structured experiments in isolated villages.


Journal

Science (New York, N.Y.)
ISSN: 1095-9203
Titre abrégé: Science
Pays: United States
ID NLM: 0404511

Informations de publication

Date de publication:
03 May 2024
Historique:
medline: 2 5 2024
pubmed: 2 5 2024
entrez: 2 5 2024
Statut: ppublish

Résumé

Certain people occupy topological positions within social networks that enhance their effectiveness at inducing spillovers. We mapped face-to-face networks among 24,702 people in 176 isolated villages in Honduras and randomly assigned villages to targeting methods, varying the fraction of households receiving a 22-month health education package and the method by which households were chosen (randomly versus using the friendship-nomination algorithm). We assessed 117 diverse knowledge, attitude, and practice outcomes. Friendship-nomination targeting reduced the number of households needed to attain specified levels of village-wide uptake. Knowledge spread more readily than behavior, and spillovers extended to two degrees of separation. Outcomes that were intrinsically easier to adopt also manifested greater spillovers. Network targeting using friendship nomination effectively promotes population-wide improvements in welfare through social contagion.

Identifiants

pubmed: 38696582
doi: 10.1126/science.adi5147
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

eadi5147

Auteurs

Edoardo M Airoldi (EM)

Department of Statistics, Operations, and Data Science, Fox School of Business, Temple University, Philadelphia, PA 19122, USA.
Data Science Institute, Temple University, Philadelphia, PA 19122, USA.

Nicholas A Christakis (NA)

Yale Institute for Network Science, Yale University, New Haven, CT 06520, USA.
Department of Sociology, Yale University, New Haven, CT 06520, USA.
Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA.

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Classifications MeSH