Background
Complexity terms | |
Agents | Individual components that make up a system; people who act independently in social systems. Here they are the individual members of Australian Genomics. |
Complex adaptive system (CAS) | Term used for a collection of agents that interact dynamically and whose interactions and interdependencies may lead to learning, adaptation and emergent behaviours. |
Phase transition | A time when the system undergoes a crucial change or reaches a tipping point in which a significant transformation in how agents are organised or interact starts. This can alter the system, or the context in which the agents operate. |
Self-organisation | The tendency for agents in a CAS to interact in certain ways and form semi-formal groups without undue outside direction. |
Social network terms | |
Betweenness centrality | A measure of the influence of an actor in connecting others in the network. Actors with high betweenness centrality lie most often on the shortest path between other nodes. Betweenness centrality positions the actor to be a go-between or broker. |
Centralisation | A network measure that shows how dominated the whole network is by one or more nodes in terms of their ties. Low centralisation indicates a more even distribution of ties. |
Density | The proportion of ties found across a network per the number of possible ties. Expressed as a number between 0 and 1.0, when 1.0 means all possible ties are present (everyone is connected to everyone else). |
In-degree | Number of ties directed to a node, i.e. the number of times a particular individual is nominated by others as having that relationship with them. A measure of influence, importance or accessibility. |
Nodes | Agents or individuals. Depicted as points or small circles in sociograms |
Out-degree | Number of ties a particular node directs to other nodes, i.e. the number of other people a particular individual nominates as having that relationship to them. A measure of connectedness. |
Sociogram | A graphical depiction of the relationship data in a social network study collected from individuals and then collated. Based on graph theory, parameters can be computed from the aggregated data. |
Ties | The relationship of interest in a social network study. Depicted as a line between nodes. Two nodes are said to be tied if one or both acknowledge the relationship. |
Developments in Australia
Social network studies
Aims
Methods
Design
Participants, recruitment and confidentiality
Measures
Relational data
As the number of members in Australian Genomics was so large, we structured the roster of names by Program working group, Flagship project, or other group (e.g. the National Steering Committee, Community Advisory Group). Participants were asked to select the group, or groups in which they took part. This gave them access to the list of people involved in that group who we expected would be their most likely collaborators. There was no limit to the number of groups that could be selected. The final choice in this section was an alphabetical list of everyone in Australian Genomics for people who were not sure which group a working colleague was listed. Students were not listed by name in the roster of Australian Genomic members as a complete list of students was not at that time available. Participants who were working with students on Australian Genomics projects were asked to name them in a free text box.Please work your way down the list [of names] and indicate who you work with on Australian Genomics projects. By “work with” we mean in the context of genomics – shared care of patients, worked in the same lab, been involved in research together, participated in a working group together, had a phone call about Australian Genomics etc. We do not mean people whom you know only by reputation (e.g., heard them speak at a conference, read a journal article authored by them). We are trying to capture the idea of a socio-professional genomic community, capable of collective learning: a knowledge exchange network.
Sources of information
Analysis
Ethics
Results
Participants
Total (N) | Respondents (n, %) | Non-respondents (n, %) |
χ
2
| |
---|---|---|---|---|
Females | 202 | 122, (60.39%) | 80, (39.60%) | χ2(1, N = 384) = 1.17, p = .28 |
Medical specialists | 73 | 25, (34.25%) | 48, (65.75%) | χ2(1, N = 384) = 20.52, p = < .05* |
Genetic specialists | 94 | 71, (75.79%) | 23, (24.21%) | χ2(1, N = 384) = 16.02, p = < .05* |
Medical scientists | 100 | 52, (52.94%) | 48, (44.12%) | χ2(1, N = 384) = 1.87, p = .17 |
Researcher^ | 42 | 27, (64.29%) | 15, (35.71%) | χ2(1, N = 384) = 0.81, p = .37 |
Other | 75 | 47, (62.67%) | 28, (37.33%) | χ2(1, N = 384) = 0.90, p = .34 |
Relational data
Australian Genomic members
Parameter | Australian Genomics learning community 2018 | “Knew before” network (pre-2016) | “Met through” network | External collaborators in Australia |
---|---|---|---|---|
Number of nodes | 384 | 384 | 384 | 412 |
Number of informants* | 209 | 203 | 174 | 93 |
Number of ties | 6381 | 2925 | 3351 | 464 |
Number of isolates | 5 | 27 | 20 | NA** |
Highest in-degree | 91 | 44 | 75 | 7 |
Highest out-degree | 354 | 87 | 338 | 10*** |
Centralisation | 0.886 | 0.208 | 0.864 | NA** |
Density | 0.043 | 0.020 | 0.023 | 0.003 |
External collaborators
Key players
Network parameter/attribute | Measure | Profession/role | State |
---|---|---|---|
In-degree/most influential | 91 | Australian Genomics Manager | National |
82 | Clinical geneticist | Victoria | |
73 | Clinical geneticist | Victoria | |
Out-degree/most connected | 354 | Australian Genomics Manager | National |
260 | Operational staff | National | |
210 | Project Officer | South Australia | |
207 | Clinical geneticist | Victoria | |
162 | Project Officer | National | |
Betweenness centrality/brokers | 13.39 | Australian Genomics Manager | National |
5.59 | Clinical geneticist | Victoria | |
3.02 | Operational staff | National |