Difference between revisions of "Template:Article of the week"

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'''"[[Journal:Big data in the era of health information exchanges: Challenges and opportunities for public health|Big data in the era of health information exchanges: Challenges and opportunities for public health]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


Public health surveillance of communicable diseases depends on timely, complete, accurate, and useful data that are collected across a number of health care and public health systems. [[Health information exchange]]s (HIEs) which support electronic sharing of data and [[information]] between health care organizations are recognized as a source of "big data" in health care and have the potential to provide public health with a single stream of data collated across disparate systems and sources. However, given these data are not collected specifically to meet public health objectives, it is unknown whether a public health agency’s (PHA’s) secondary use of the data is supportive of or presents additional barriers to meeting disease reporting and surveillance needs. To explore this issue, we conducted an assessment of big data that is available to a PHA—[[Public health laboratory|laboratory]] test results and clinician-generated notifiable condition report data—through its participation in an HIE. ('''[[Journal:Big data in the era of health information exchanges: Challenges and opportunities for public health|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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