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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 vanGanns eJHI2015 9-1.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png|240px]]</div>
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''
'''"[[Journal:The development of the Public Health Research Data Management System|The development of the Public Health Research Data Management System]]"'''


The design and development of the Public Health Research Data Management System highlights how it is possible to construct an [[information]] system, which allows greater access to well, preserved public health research data to enable it to be reused and shared. The Public Health Research Data Management System (PHRDMS) manages clinical, health service, community and survey research data within a secure web environment. The conceptual model under pinning the PHRDMS is based on three main entities: participant, community and health service. The PHRDMS was designed to provide data management to allow for data sharing and reuse. The system has been designed to enable rigorous research and ensure that: data that are unmanaged be managed, data that are disconnected be connected, data that are invisible be findable, data that are single use be reusable, within a structured collection. The PHRDMS is currently used by researchers to answer a broad range of policy relevant questions, including monitoring incidence of renal disease, cardiovascular disease, diabetes and mental health problems in different risk groups. ('''[[Journal:The development of the Public Health Research Data Management System|Full article...]]''')<br />
The introduction of [[ChatGPT]] has fuelled a public debate on the appropriateness of using generative [[artificial intelligence]] (AI) ([[large language model]]s or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (''N'' = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... ('''[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Full article...]]''')<br />
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Revision as of 15:26, 20 May 2024

Fig1 Niszczota EconBusRev23 9-2.png

"Judgements of research co-created by generative AI: Experimental evidence"

The introduction of ChatGPT has fuelled a public debate on the appropriateness of using generative artificial intelligence (AI) (large language models or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (N = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... (Full article...)
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