Acquiring the lexicon and grammar of universal kinship: Supplementary material

Blythe, Joe; Tunmuck, Jeremiah; Mitchell, Alice; Rácz, Péter

doi:10.1353/lan.2020.0056

Cited by 2 publications

(3 citation statements)

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“…In compliance with the 2010 Plain Writing Act, AHRQ requires the use of plain language when the agency creates content for the public [6]. Likewise, Cochrane, which produces the leading database for healthcare‐related systematic reviews, requires PLSs for all reviews published in the Cochrane Library [7].…”

Section: Introductionmentioning

confidence: 99%

“…Key components of PLSs include the use of the active voice, logical organization, short sentences and paragraphs, avoidance of jargon and technical terms, and the definition of such terms, if they must be used [4, 7, 8]. In general, PLSs should be written at the reading level of their intended audience.…”

Section: Introductionmentioning

confidence: 99%

“…In general, PLSs should be written at the reading level of their intended audience. While specific reading level guidance is sometimes given [4], most current guidance, such as that developed by AHRQ and Cochrane [7], note that reading level varies by the context of the audience so should be tailored to the context, and that existing formulas used to assess reading levels are best used as tools to diagnose overly complex text [9]. Other expectations of plain language materials created for healthcare communication include layout, the use of tables or graphics, and specific typography [8].…”

Section: Introductionmentioning

confidence: 99%

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The use of a large language model to create plain language summaries of evidence reviews in healthcare: A feasibility study

Ovelman,

Kugley,

Gartlehner

et al. 2024

Cochrane Evidence Synthesis and Methods

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IntroductionPlain language summaries (PLSs) make complex healthcare evidence accessible to patients and the public. Large language models (LLMs) may assist in generating accurate, readable PLSs. This study explored using the LLM Claude 2 to create PLSs of evidence reviews from the Agency for Healthcare Research and Quality (AHRQ) Effective Health Care Program.MethodsWe selected 10 evidence reviews published from 2021 to 2023, representing a range of methods and topics. We iteratively developed a prompt to guide Claude 2 in creating PLSs which included specifications for plain language, reading level, length, organizational structure, active voice, and inclusive language. PLSs were assessed for adherence to prompt specifications, comprehensiveness, accuracy, readability, and cultural sensitivity.ResultsAll PLSs met the word count. We judged one PLS as fully comprehensive; seven mostly comprehensive. We judged two PLSs as fully capturing the PICO elements; five with minor PICO errors. We judged three PLSs as accurately reporting the results; and four with minor result errors. We judged three PLSs as having major result errors for incorrectly reporting total participants. Five PLSs met the target 6th to 8th grade reading level. Passive voice use averaged 16%. All PLSs used inclusive language.ConclusionsLLMs show promise for assisting in PLS creation but likely require human input to ensure accuracy, comprehensiveness, and the appropriate nuances of interpretation. Iterative prompt refinement may improve results and address the needs of specific reviews and audiences. As text‐only summaries, the AI‐generated PLSs could not meet all consumer communication criteria, such as textual design and visual representations. Further testing should include consumer reviewers and explore how to best leverage LLM support in drafting PLS text for complex evidence reviews.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The use of a large language model to create plain language summaries of evidence reviews in healthcare: A feasibility study

Ovelman,

Kugley,

Gartlehner

et al. 2024

Cochrane Evidence Synthesis and Methods

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The Ontogeny of Kinship Categorization

Mitchell

Jordan

2021

J. Cogn. Cult.

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Human kinship systems play a central role in social organization, as anthropologists have long demonstrated. Much less is known about how cultural schemas of relatedness are transmitted across generations. How do children learn kinship concepts? To what extent is learning affected by known cross-cultural variation in how humans classify kin? This review draws on research in developmental psychology, linguistics, and anthropology to present our current understanding of the social and cognitive foundations of kinship categorization. Amid growing interest in kinship in the cognitive sciences, the paper aims to stimulate new research on the ontogeny of kinship categorization, a rich domain for studying the nexus of language, culture, and cognition. We introduce an interdisciplinary research toolkit to help streamline future research in this area.

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Acquiring the lexicon and grammar of universal kinship: Supplementary material

Cited by 2 publications

References 0 publications

The use of a large language model to create plain language summaries of evidence reviews in healthcare: A feasibility study

The use of a large language model to create plain language summaries of evidence reviews in healthcare: A feasibility study

The Ontogeny of Kinship Categorization

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