{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T21:31:13Z","timestamp":1791840673645,"version":"4.3.4"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":193,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100011827","name":"Guangxi Medical University","doi-asserted-by":"publisher","award":["YYZS2020025"],"award-info":[{"award-number":["YYZS2020025"]}],"id":[{"id":"10.13039\/501100011827","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Nursing Management"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Increasing work pressure has elevated burnout risk among Chinese nurses; identifying associated factors and developing predictive models are essential for early intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>This study aims to develop a nurse burnout prediction model based on machine learning algorithms in order to assist nursing management in the future.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>A cross\u2010sectional online survey of 1391 Chinese nurses (burnout rate: 75.7%) was conducted from June to September 2025 using snowball sampling. Burnout was measured by MBI\u2010GS, and 25 candidate variables were collected. Data were split 80\/20 into training\/validation sets. After variable selection (Boruta \u2229 Group LASSO), seven algorithms were compared. Model evaluation used AUC, PR\u2010AUC, calibration, DCA, and SHAP. The optimal threshold (Youden index) defined three risk tiers, and a Shiny web calculator was developed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Random forest achieved the best test performance (AUC\u2009=\u20090.879, PR\u2010AUC\u2009=\u20090.940, and sensitivity\u2009=\u20090.870). Key features included colleague relationship, work duration, exercise frequency, and night shift hours. SHAP analysis showed that harmonious colleague relationships, longer work duration, and regular exercise were associated with lower predicted risk, whereas longer night shifts and frequent training\/exams were associated with higher predicted risk. Model calibration (HL\n                      <jats:italic>p<\/jats:italic>\n                      = 0.3505) and DCA confirmed clinical benefit. A Shiny web calculator was developed.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The random forest model effectively predicts nurse job burnout with good discrimination and calibration. The Shiny web calculator developed based on this model provides a practical tool for early identification and stratified intervention in nursing management. External validation is needed to further confirm generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance to Clinical Practice<\/jats:title>\n                    <jats:p>The predictive model can be integrated into hospital human resource and nursing information systems to enable automatic assessment of burnout risk. This facilitates early identification of high\u2010risk nurses, allowing managers to implement timely interventions such as adjusted schedules and psychological support. The deployed online calculator provides an accessible tool for dynamic monitoring and proactive management, supporting data\u2010driven decision\u2010making to improve nursing workforce stability and healthcare service quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Permission to Reproduce Material From Other Sources<\/jats:title>\n                    <jats:p>No materials from other sources were used in this manuscript that require permission.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Reporting Guidelines<\/jats:title>\n                    <jats:p>This study followed the TRIPOD\u2009+\u2009AI statement. The completed TRIPOD\u2009+\u2009AI checklist is provided as supporting information.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/jonm\/8179894","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T03:30:48Z","timestamp":1783999848000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Identifying Key Predictors and Developing a Machine Learning Model for Nurse Burnout in China"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0361-3138","authenticated-orcid":false,"given":"Zirong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Gan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinbi","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5641-936X","authenticated-orcid":false,"given":"Yanping","family":"Ying","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"e_1_2_18_1_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.36469"},{"key":"e_1_2_18_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2020.100565"},{"key":"e_1_2_18_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iccn.2024.103660"},{"key":"e_1_2_18_4_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1225431"},{"key":"e_1_2_18_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/jonm.12150"},{"key":"e_1_2_18_6_2","doi-asserted-by":"publisher","DOI":"10.2147\/rmhp.s503613"},{"key":"e_1_2_18_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnurstu.2024.104962"},{"key":"e_1_2_18_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnurstu.2021.103973"},{"key":"e_1_2_18_9_2","doi-asserted-by":"publisher","DOI":"10.1080\/02648725.2023.2168910"},{"key":"e_1_2_18_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apnr.2015.12.007"},{"key":"e_1_2_18_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12912-022-00831-3"},{"key":"e_1_2_18_12_2","doi-asserted-by":"publisher","DOI":"10.1111\/jocn.17210"},{"key":"e_1_2_18_13_2","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/9428519"},{"key":"e_1_2_18_14_2","doi-asserted-by":"publisher","DOI":"10.1027\/1015-5759\/a000797"},{"key":"e_1_2_18_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.burn.2014.09.001"},{"key":"e_1_2_18_16_2","doi-asserted-by":"publisher","DOI":"10.21772\/ripo.v33n2a04"},{"key":"e_1_2_18_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103375"},{"key":"e_1_2_18_18_2","doi-asserted-by":"publisher","DOI":"10.47852\/bonviewjcce2202238"},{"key":"e_1_2_18_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2023.101442"},{"key":"e_1_2_18_20_2","doi-asserted-by":"crossref","unstructured":"SchapireR. 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