{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:45:15Z","timestamp":1791895515290,"version":"4.3.4"},"reference-count":27,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100009633","name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["R00HD107030"],"award-info":[{"award-number":["R00HD107030"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000056","name":"National Institute of Nursing Research","doi-asserted-by":"publisher","award":["T32NR009759"],"award-info":[{"award-number":["T32NR009759"]}],"id":[{"id":"10.13039\/100000056","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000056","name":"National Institute of Nursing Research","doi-asserted-by":"publisher","award":["T32NR022597"],"award-info":[{"award-number":["T32NR022597"]}],"id":[{"id":"10.13039\/100000056","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Biological Research For Nursing"],"abstract":"<jats:p>Reproducibility and replicability are our scientific and ethical mandate, and the foundation of trust in nursing science and research. Both rest on transparent documentation of how data were actually analyzed. The rising use of generative artificial intelligence (GenAI) in research intersects with this mandate, with the potential to either support or undermine it, yet practical guidance for conducting GenAI-assisted quantitative analysis remains limited. This gap is particularly relevant for nurse scientists, who increasingly work with complex, biologically-informed datasets spanning genomics, symptoms, and patient outcomes. To address this gap, we present a practice-based guide for partnering with GenAI in quantitative data analysis in nursing research, grounded in a nursing-focused genomic research example. We cover GenAI literacy spanning three dimensions (technical, practical, and ethical) and a two-phase workflow for human-GenAI collaboration spanning planning and pre-specification through code development, formal analysis, translation, and human review and disclosure of GenAI use. The core principle is using GenAI as a tool, not a decision-maker, as scientific integrity requires that humans retain interpretive authority. We further propose a synthetic-data-first approach as a privacy safeguard. We illustrate these concepts through a genetics example, though the principles and workflow generalize to many quantitative nursing research contexts.<\/jats:p>","DOI":"10.1177\/10998004261489817","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T10:59:31Z","timestamp":1791629971000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Partnering With Generative Artificial Intelligence for Quantitative Data Analysis in Nursing Research: A Practical Workflow for Ethical Collaboration"],"prefix":"10.1177","author":[{"given":"Gina M.","family":"Gehling","sequence":"first","affiliation":[{"name":"University of Pittsburgh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7690-5485","authenticated-orcid":false,"given":"Lacey W.","family":"Heinsberg","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"},{"name":"University of Pittsburgh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,10]]},"reference":[{"issue":"1","key":"e_1_3_7_2_1","first-page":"1","article-title":"Editorial\u2014The use of large language models in science: Opportunities and challenges","volume":"9","author":"Almarie B.","year":"2023","unstructured":"Almarie B., Teixeira P. E. P., Pacheco-Barrios K., Rossetti C. A., Fregni F. (2023). Editorial\u2014The use of large language models in science: Opportunities and challenges. 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(2026). Use of artificial intelligence in publishing. https:\/\/www.icmje.org\/recommendations\/browse\/artificial-intelligence\/"},{"key":"e_1_3_7_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3631802.3631806"},{"key":"e_1_3_7_12_1","doi-asserted-by":"publisher","DOI":"10.1037\/xge0001213"},{"key":"e_1_3_7_13_1","article-title":"A retrospective analysis of 400 publications reveals patterns of irreproducibility across an entire life sciences research field","volume":"15","author":"Lemaitre J.","year":"2026","unstructured":"Lemaitre J., Popelka D., Ribotta B., Westlake H., Chakrabarti S., Xiaoxue L., Hanson M. A., Jiang H., Cara F. D., Kurant E., David F., Lemaitre B. (2026). A retrospective analysis of 400 publications reveals patterns of irreproducibility across an entire life sciences research field. eLife, 15, RP108403. https:\/\/doi.org\/10.7554\/eLife.108403","journal-title":"eLife"},{"key":"e_1_3_7_14_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjebm-2025-113825"},{"key":"e_1_3_7_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07146-0"},{"key":"e_1_3_7_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43465-024-01258-5"},{"key":"e_1_3_7_17_1","first-page":"39","volume-title":"Reproducibility and replicability in science","author":"National Academies of Sciences, Engineering, and Medicine","year":"2019","unstructured":"National Academies of Sciences, Engineering, and Medicine. (2019). Understanding reproducibility and replicability. In Reproducibility and replicability in science (pp. 39\u201354). 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