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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab4 Wholey FrontPubHealth2019 6.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:Developing workforce capacity in public health informatics: Core competencies and curriculum design|Developing workforce capacity in public health informatics: Core competencies and curriculum design]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


We describe a master’s level [[public health informatics]] (PHI) curriculum to support workforce development. Public health decision-making requires intensive [[information management]] to organize responses to health threats and develop effective health education and promotion. PHI competencies prepare the public health workforce to design and implement these information systems. The objective for a master's and certificate in PHI is to prepare public health informaticians with the competencies to work collaboratively with colleagues in public health and other health professions to design and develop information systems that support population health improvement. The PHI competencies are drawn from computer, information, and organizational sciences. A curriculum is proposed to deliver the competencies, and the results of a pilot PHI program are presented. Since the public health workforce needs to use information technology effectively to improve population health, it is essential for public health academic institutions to develop and implement PHI workforce training programs. ('''[[Journal:Developing workforce capacity in public health informatics: Core competencies and curriculum design|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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