@article{nokey,

title = {Planning, implementing and governing systems-based co-creation: the DISCOVER framework},

author = {Niamh Smith and Michail Georgiou and Mohammad S. Jalali and Sebastien Chastin },

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2024/02/Smith-2024.pdf},

year  = {2024},

date = {2024-01-08},

urldate = {2024-01-08},

journal = {Health Research Policy and Systems},

volume = {22},

issue = {6},

abstract = {Increasingly, public health faces challenges requiring complex, multifaceted and multi-sectoral responses. This calls for systems-based approaches that facilitate the kind of collective and collaborative thinking and working required to address complexity. While the literature on systems thinking, system dynamics and the associated methodologies is extensive, there remains little clear guidance on how to plan, govern and implement participatory systems approaches within a co-creation process.},

keywords = {Methods},

pubstate = {published},

tppubtype = {article}

}

@article{631222,

title = {A Flexible Method for Aggregation of Prior Statistical Findings},

author = {Hazhir Rahmandad and Mohammad S Jalali and Kamran Paynabar},

url = {https://mj-lab.mgh.harvard.edu/wordpress/research/},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {PLOS ONE},

volume = {12},

pages = {e0175111},

abstract = {Rapid growth in scientific output requires methods for quantitative synthesis of prior research, yet current meta-analysis methods limit aggregation to studies with similar designs. Here we describe and validate Generalized Model Aggregation (GMA), which allows researchers to combine prior estimated models of a phenomenon into a quantitative meta-model, while imposing few restrictions on the structure of prior models or on the meta-model. In an empirical validation, building on 27 published equations from 16 studies, GMA provides a predictive equation for Basal Metabolic Rate that outperforms existing models, identifies novel nonlinearities, and estimates biases in various measurement methods. Additional numerical examples demonstrate the ability of GMA to obtain unbiased estimates from potentially mis-specified prior studies. Thus, in various domains, GMA can leverage previous findings to compare alternative theories, advance new models, and assess the reliability of prior studies, extending meta-analysis toolbox to many new problems.},

note = {More info about GMA},

keywords = {Methods},

pubstate = {published},

tppubtype = {article}

}