Evidence map›Paper›PMID 41152870›Full record

ArticleHealth research policy and systems2025

From complexity to parsimony: A systems thinking validation of the multiple streams framework in abortion policy agenda setting.

Gabriela Alvarado

Abstract read
In one paragraph

Article in Health research policy and systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

1 author.

Gabriela AlvaradoRAND Corporation, 1200 South Hayes St, Arlington, VA, 22202-5050, USA. alvarado@rand.org.

Funding

Pardee RAND Graduate School Dissertation award 2022-2023
6 · The paper itself

Abstract

backgroundAbortion policy is a highly contested area of health policy. Despite international recognition of abortion as a human right, legal restrictions persist in many countries, and recent decades have seen both liberalization and retrenchment of abortion laws. While much research has examined the politics and outcomes of abortion policy reform, less attention has been paid to the upstream process of how abortion emerges on the policy agenda. This study addresses this gap by focusing on agenda setting for abortion policy, using Kingdon's multiple streams framework (MSF) and systems thinking.

methodsThis exploratory study integrates MSF with systems thinking and causal loop diagramming to map the dynamic interactions among government characteristics, policy communities, policy-maker attributes and external events. A purposive review of 19 key works from the MSF literature was conducted, selected for their theoretical contributions and detailed descriptions of stream interactions. Qualitative text coding and quotation analysis were used to identify causal relationships, which were then aggregated into a causal loop diagram. Model validation focused on micro-structure elements, and the framework was tested against two case studies: Ireland and Nicaragua.

resultsAnalysis yielded 167 unique elements and 338 causal links, distilled into 81 key variables. The causal loop diagram demonstrates that convergence of the problem, policy and politics streams is shaped by reinforcing and balancing feedbacks, rather than random chance. Key factors influencing agenda setting include party institutionalization, policy entrepreneur effectiveness, social inequality and the gravity of focusing events. The case studies illustrate how variations in political institutions, mobilization efforts and external events can lead to divergent policy trajectories.

conclusionsThis study provides theoretical validation that the parsimonious MSF can account for the complexity of abortion policy agenda setting when integrated with systems thinking. The causal loop diagram identifies actionable leverage points for advocacy and policy reform and offers a dynamic, testable model for understanding agenda setting in contentious policy domains. These findings bridge theoretical innovation with practical relevance, laying a foundation for future empirical research and offering insights for scholars, advocates and decision-makers seeking to influence the policy agenda for abortion and other complex health issues.

Indexed as

Abortion, InducedHealth PolicyPolicy MakingPoliticsFemaleHuman RightsHumansIrelandNicaraguaPregnancySystems Analysis

Identifiers

PMID41152870
PMCPMC12570440

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.