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Advancing Science while Training Undergraduates: Recommendations from a Collaborative Biology Research Network.

ABSTRACT: Biology research is becoming increasingly dependent on large-scale, "big data," networked research initiatives. At the same time, there has been a corresponding effort to expand undergraduate participation in research to benefit student learning and persistence in science. This essay examines the confluence of this trend through eight years of a collaboration within a successful biology research network that explicitly incorporates undergraduates into large-scale scientific research. We draw upon interviews with faculty in this network to consider the interplay of scientific and pedagogical objectives at the heart of this undergraduate-focused network research project. We identify ways that this network has expanded and diversified access to scientific knowledge production for faculty and students and examine a goal conflict that emerged around the dual objectives of mentoring emerging scientists while producing high-quality scientific data for the larger biology community. Based on lessons learned within this network, we provide three recommendations that can support institutions and faculty engaging in networked research projects with undergraduates: (1) establish rigorous protocols to ensure data and database quality, (2) protect personnel time to coordinate network and scientific processes, and (3) select appropriate partners and establish explicit expectations for specific collaborations.

SUBMITTER: Jensen-Ryan D

PROVIDER: S-EPMC8693944 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLogic models are commonly used in evaluations to represent the causal processes through which interventions produce outcomes, yet significant debate is currently taking place over whether they can describe complex interventions which adapt to context. This paper assesses the logic models used in healthcare research from a complexity perspective. A typology of existing logic models is proposed, as well as a formal methodology for deriving more flexible and dynamic logic models.AnalysisVarious logic model types were tested as part of an evaluation of a complex Patient Experience Toolkit (PET) intervention, developed and implemented through action research across six hospital wards/departments in the English NHS. Three dominant types of logic model were identified, each with certain strengths but ultimately unable to accurately capture the dynamics of PET. Hence, a fourth logic model type was developed to express how success hinges on the adaption of PET to its delivery settings. Aspects of the Promoting Action on Research Implementation in Health Services (PARIHS) model were incorporated into a traditional logic model structure to create a dynamic "type 4" logic model that can accommodate complex interventions taking on a different form in different settings.ConclusionLogic models can be used to model complex interventions that adapt to context but more flexible and dynamic models are required. An implication of this is that how logic models are used in healthcare research may have to change. Using logic models to forge consensus among stakeholders and/or provide precise guidance across different settings will be inappropriate in the case of complex interventions that adapt to context. Instead, logic models for complex interventions may be targeted at facilitators to enable them to prospectively assess the settings they will be working in and to develop context-sensitive facilitation strategies. Researchers should be clear as to why they are using a logic model and experiment with different models to ensure they have the correct type.

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Advancing Science while Training Undergraduates: Recommendations from a Collaborative Biology Research Network.

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