{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:23:31Z","timestamp":1786123411061,"version":"build-2736575974"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This study aimed to examine the symptom networks of anxiety and occupational burnout among newly hired nurses and to identify central, bridge, and longitudinally influential anxiety symptoms or burnout dimensions that may inform targeted interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were collected from newly hired nurses at a tertiary hospital in China between July 2023 and July 2024. Anxiety and burnout were assessed using the Generalized Anxiety Disorder-7 and Maslach Burnout Inventory -Human Services Survey, respectively. Contemporaneous networks at three time points and cross - lagged panel models (for longitudinal networks) were built, with centrality indices evaluating network features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>1) Anxiety prevalence: 4.3%\u201314.4%; burnout detection rate: 47.4%\u201354.7%. 2) Contemporaneous networks: \u201ctrouble relaxing\u201d, \u201cuncontrollable worry\u201d, \u201cafraid something will happen\u201d served as the central anxiety symptoms at the three time points, respectively; \u201cdepersonalization\u201d, \u201cemotional exhaustion\u201d were central burnout dimensions, with \u201cemotional exhaustion\u201d having highest bridge strength. 3) Occupational burnout dimensions showed high autoregressive coefficients. 4) Temporal networks showed that from T1 to T2, temporal associations were mainly concentrated within anxiety symptoms, with \u201cuncontrollable worry\u201d and \u201cworry too much\u201d showing the highest in-strength value. From T2 to T3, stronger associations emerged between anxiety and burnout, with the strongest edge from emotional exhaustion to later nervousness or anxiety; within burnout dimensions, lower personal accomplishment was associated with higher subsequent depersonalization, and depersonalization was related to later emotional exhaustion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Worry-related symptoms, irritability, emotional exhaustion, and depersonalization among newly hired nurses played important roles in the networks. Early screening and targeted interventions should focus on reducing anxiety and uncertainty in the early stage, while greater attention should be paid to occupational burnout as newly hired nurses gradually adapt to clinical work.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1884139","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:31:11Z","timestamp":1786116671000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal relationship between anxiety symptoms and occupational burnout dimensions among newly hired nurses: a cross-lagged network analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhilin","family":"Bian","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University","place":["Chengdu, 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