The Interdisciplinary Health Care Team: A Handbook

Coyle, Nessa; Ducanis, Alex J.; Golin, Anne K.

doi:10.2307/3462525

Cited by 10 publications

(5 citation statements)

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“…An extensive body of research has focused on the key factors that affect team performance. Works from a range of disciplines identified features like regular communication, coordination, distinctive roles, interdependent tasks, and shared norms as the building blocks of effective teams [1][2][3][4]28]. Several models have been proposed and evaluated in different contexts.…”

Section: Related Workmentioning

confidence: 99%

“…Computation-The isolated subgraphs are mapped to low-dimensional continuous representations exploiting a mixed graph convolutional architecture. Firstly, a single graph attentional layer 2 (GAT) [22,62] transforms the node features X into higher-level representations h (1) by pooling information from nodes' 1-hop neighborhood and by learning a self-attention mechanism [22,61]. Together with embeddings h (1) , the learned layer returns attention coefficients, α vu , that indicate the importance of node u's features to node v, if they are connected.…”

Section: Topology Channelmentioning

confidence: 99%

“…What makes a team effective is a long-standing problem widely studied across disciplines and applicative contexts. Several factors such as communication, coordination, distinctive roles, interdependent tasks, shared norms, personality traits, and diversity are relevant aspects shaping team performance [1][2][3][4][5][6]. Yet, our understanding of teams as evolving systems of interacting individuals as well as the relation between team composition and performance is still partial [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…The intuition is that teams could be more than the sum of their parts. Perspectives from Complexity and Network Science offer natural frameworks to capture and investigate this direction [1][2][3][4][5][11][12][13][14][15][16]. Within these approaches, teams' performance has been linked to three effects.…”

Section: Introductionmentioning

confidence: 99%

“…The first are topological effects. The internal structure of a team, emerging from the interactions of its members, plays a crucial role in determining performance [1][2][3][4][5]17]. The second are centrality effects.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modeling teams performance using deep representational learning on graphs

Carli,

Foini,

Gozzi

et al. 2024

EPJ Data Sci.

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Most human activities require collaborations within and across formal or informal teams. Our understanding of how the collaborative efforts spent by teams relate to their performance is still a matter of debate. Teamwork results in a highly interconnected ecosystem of potentially overlapping components where tasks are performed in interaction with team members and across other teams. To tackle this problem, we propose a graph neural network model to predict a team’s performance while identifying the drivers determining such outcome. In particular, the model is based on three architectural channels: topological, centrality, and contextual, which capture different factors potentially shaping teams’ success. We endow the model with two attention mechanisms to boost model performance and allow interpretability. A first mechanism allows pinpointing key members inside the team. A second mechanism allows us to quantify the contributions of the three driver effects in determining the outcome performance. We test model performance on various domains, outperforming most classical and neural baselines. Moreover, we include synthetic datasets designed to validate how the model disentangles the intended properties on which our model vastly outperforms baselines.

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