{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T03:04:14Z","timestamp":1777863854242,"version":"3.51.4"},"reference-count":47,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":118,"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"}],"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>Purpose<\/jats:title>\n                    <jats:p>This study aims to analyze the critical variables and gap variables affecting hospital nurses\u2019 job satisfaction and propose improvement strategies based on the knowledge domains of nursing decision\u2010makers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study, conducted between September and October 2022 during the dynamic adjustment phase of COVID\u201019 prevention and control in China, was based on the McCloskey\/Mueller Satisfaction Scale (MMSS) and developed a hybrid machine learning and decision analysis tool model. The random forest (RF) method was used to estimate the importance of each variable in the data, and the importance\u2010performance analysis (IPA) was used to identify critical gap variables and propose improvement strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The RF analysis (OOB error rate\u2009=\u200917.93%) identified \u201cDecision\u2010making\u201d (\n                      <jats:italic>C<\/jats:italic>\n                      <jats:sub>30<\/jats:sub>\n                      , importance score\u2009=\u20090.053) and \u201cControl\u2010work conditions\u201d (\n                      <jats:italic>C<\/jats:italic>\n                      <jats:sub>29<\/jats:sub>\n                      , importance score\u2009=\u20090.067) as the most influential factors (critical variables) in determining nurses\u2019 job satisfaction. The IPA analysis identified\n                      <jats:italic>C<\/jats:italic>\n                      <jats:sub>30<\/jats:sub>\n                      as the most critical gap variable, indicating a significant need to improve nurses\u2019 involvement in hospital decision\u2010making processes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>To improve nurse job satisfaction and retention, hospital decision\u2010makers and nursing departments should implement policies that enhance nurses\u2019 involvement in decision\u2010making, particularly those with experience in pandemic\u2010related healthcare challenges. Addressing these factors could foster a more supportive and resilient nursing work environment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/jonm\/7240298","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T05:32:22Z","timestamp":1777527142000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of Critical Variables and Critical Gap Variables in Hospital Nurses\u2019 Job Satisfaction During the Dynamic Adjustment Phase of COVID\u201019 Prevention in China: A Hybrid Machine Learning and Decision Analysis 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