↓ Skip to main content

Understanding the dynamics of the Seguro Popular de Salud policy implementation in Mexico from a complex adaptive systems perspective

Overview of attention for article published in Implementation Science, May 2016
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)

Mentioned by

twitter
3 X users
facebook
1 Facebook page

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
109 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Understanding the dynamics of the Seguro Popular de Salud policy implementation in Mexico from a complex adaptive systems perspective
Published in
Implementation Science, May 2016
DOI 10.1186/s13012-016-0439-x
Pubmed ID
Authors

Gustavo Nigenda, Luz María González-Robledo, Clara Juárez-Ramírez, Taghreed Adam

Abstract

In 2003, Mexico's Seguro Popular de Salud (SPS), was launched as an innovative financial mechanism implemented to channel new funds to provide health insurance to 50 million Mexicans and to reduce systemic financial inequities. The objective of this article is to understand the complexity and dynamics that contributed to the adaptation of the policy in the implementation stage, how these changes occurred, and why, from a complex and adaptive systems perspective. A complex adaptive systems (CAS) framework was used to carry out a secondary analysis of data obtained from four SPS's implementation evaluations. We first identified key actors, their roles, incentives and power, and their responses to the policy and guidelines. We then developed a causal loop diagram to disentangle the feedback dynamics associated with the modifications of the policy implementation which we then analyzed using a CAS perspective. Implementation variations were identified in seven core design features during the first 10 years of implementation period, and in each case, the SPS's central coordination introduced modifications in response to the reactions of the different actors. We identified several CAS phenomena associated with these changes including phase transitions, network emergence, resistance to change, history dependence, and feedback loops. Our findings generate valuable lessons to policy implementation processes, especially those involving a monetary component, where the emergence of coping mechanisms and other CAS phenomena inevitably lead to modifications of policies and their interpretation by those who implement them. These include the difficulty of implementing strategies that aim to pool funds through solidarity among beneficiaries where the rich support the poor when there are no incentives for the rich to do so. Also, how resistance to change and history dependence can pose significant challenges to implementing changes, where the local actors use their significant power to oppose or modify these changes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Unknown 107 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 18%
Student > Ph. D. Student 19 17%
Researcher 15 14%
Student > Doctoral Student 9 8%
Student > Bachelor 5 5%
Other 15 14%
Unknown 26 24%
Readers by discipline Count As %
Medicine and Dentistry 28 26%
Social Sciences 17 16%
Economics, Econometrics and Finance 6 6%
Nursing and Health Professions 6 6%
Business, Management and Accounting 5 5%
Other 16 15%
Unknown 31 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 January 2020.
All research outputs
#13,309,397
of 23,773,824 outputs
Outputs from Implementation Science
#1,315
of 1,738 outputs
Outputs of similar age
#147,434
of 314,457 outputs
Outputs of similar age from Implementation Science
#32
of 34 outputs
Altmetric has tracked 23,773,824 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,738 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 314,457 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.