↓ Skip to main content

Conceptualising population health: from mechanistic thinking to complexity science

Overview of attention for article published in Emerging Themes in Epidemiology, January 2011
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#15 of 143)
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

blogs
1 blog
twitter
19 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
60 Dimensions

Readers on

mendeley
161 Mendeley
citeulike
1 CiteULike
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
Conceptualising population health: from mechanistic thinking to complexity science
Published in
Emerging Themes in Epidemiology, January 2011
DOI 10.1186/1742-7622-8-2
Pubmed ID
Authors

Saroj Jayasinghe

Abstract

The mechanistic interpretation of reality can be traced to the influential work by René Descartes and Sir Isaac Newton. Their theories were able to accurately predict most physical phenomena relating to motion, optics and gravity. This paradigm had at least three principles and approaches: reductionism, linearity and hierarchy. These ideas appear to have influenced social scientists and the discourse on population health. In contrast, Complexity Science takes a more holistic view of systems. It views natural systems as being 'open', with fuzzy borders, constantly adapting to cope with pressures from the environment. These are called Complex Adaptive Systems (CAS). The sub-systems within it lack stable hierarchies, and the roles of agency keep changing. The interactions with the environment and among sub-systems are non-linear interactions and lead to self-organisation and emergent properties. Theoretical frameworks such as epi+demos+cracy and the ecosocial approach to health have implicitly used some of these concepts of interacting dynamic sub-systems. Using Complexity Science we can view population health outcomes as an emergent property of CAS, which has numerous dynamic non-linear interactions among its interconnected sub-systems or agents. In order to appreciate these sub-systems and determinants, one should acquire a basic knowledge of diverse disciplines and interact with experts from different disciplines. Strategies to improve health should be multi-pronged, and take into account the diversity of actors, determinants and contexts. The dynamic nature of the system requires that the interventions are constantly monitored to provide early feedback to a flexible system that takes quick corrections.

Twitter Demographics

The data shown below were collected from the profiles of 19 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 161 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 5 3%
United States 4 2%
Portugal 2 1%
United Kingdom 2 1%
South Africa 1 <1%
Australia 1 <1%
Unknown 146 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 40 25%
Student > Ph. D. Student 34 21%
Student > Master 20 12%
Professor > Associate Professor 14 9%
Student > Doctoral Student 9 6%
Other 31 19%
Unknown 13 8%
Readers by discipline Count As %
Medicine and Dentistry 49 30%
Social Sciences 32 20%
Nursing and Health Professions 12 7%
Agricultural and Biological Sciences 9 6%
Psychology 5 3%
Other 29 18%
Unknown 25 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 15 March 2020.
All research outputs
#1,332,550
of 21,025,129 outputs
Outputs from Emerging Themes in Epidemiology
#15
of 143 outputs
Outputs of similar age
#11,797
of 248,725 outputs
Outputs of similar age from Emerging Themes in Epidemiology
#1
of 1 outputs
Altmetric has tracked 21,025,129 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 143 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.8. This one has done particularly well, scoring higher than 90% of its peers.
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 248,725 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them