{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T08:11:56Z","timestamp":1767168716109,"version":"build-2238731810"},"reference-count":33,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2022,4,7]]},"abstract":"\n The research was aimed to analyze the impact of epidemic pneumonia on nursing personnel\u2019s mental health under wireless network background and to improve the selection of random forest classification (RFC) algorithm parameters by the whale optimization algorithm (WOA). Besides, a total of 148 in-service nursing personnel were selected as the research objects, and 148 questionnaires were recycled effectively. The collected data were analyzed by the improved RFC algorithm. In addition, the research investigated the impacts of demographic factors on nursing personnel\u2019s mental health by the one-way variance method. The results demonstrated that the accuracy of the improved algorithm in training samples and test samples reached 83.3% and 81.6%, respectively, both of which were obviously higher than those of support vector machine (SVM) (80.1% and 79.3%, respectively) and back-propagation neural network (BPNN) (78.23% and 77.9%, respectively), and the differences showed statistical meanings\n \n \n \n \n P<\/a:mi>\n <<\/a:mo>\n 0.05<\/a:mn>\n <\/a:mrow>\n <\/a:mfenced>\n <\/a:math>\n <\/jats:inline-formula>\n . The Patient Health Questionnaire-9 (PHQ-9) showed that the depression levels of 9.46% of the included personnel were above moderate. The Generalized Anxiety Disorder (GAD-7) demonstrated that the anxiety levels of 3.38% of the included personnel were above moderate. The insomnia severity index (ISI) indicated that the insomnia levels of 3.38% of the included personnel were above moderate. The average score of male personnel (3.65) was obviously lower than that of female personnel (3.71). Besides, the average scale score of married personnel (3.78) was significantly higher than that of unmarried personnel (3.65). The average scale scores of personnel with bachelor\u2019s (3.66) and master\u2019s degrees (3.62) were obviously lower than those of personnel with junior college (3.77) and technical secondary school (3.75) diplomas. The average scale score of personnel with over 5-year work experience (3.68) was significantly lower than that of personnel working for less than five years (3.72). The average scale score of personnel with experience in responding to public emergencies (3.65) was obviously lower than that of personnel without related experience (3.74). The differences all showed statistical meaning\n \n \n \n \n P<\/f:mi>\n <<\/f:mo>\n 0.05<\/f:mn>\n <\/f:mrow>\n <\/f:mfenced>\n <\/f:math>\n <\/jats:inline-formula>\n . The results of this research revealed that the accuracy of the improved RFC algorithm was remarkably higher than that of the SVM and BPNN algorithms. Furthermore, many nursing personnel suffered from mental diseases at different levels with the impact of the epidemic. Gender, marital status, education level, and experience in responding to public emergencies were the main factors affecting nursing personnel\u2019s mental health.\n <\/jats:p>","DOI":"10.1155\/2022\/3413815","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T19:50:09Z","timestamp":1649361009000},"page":"1-11","source":"Crossref","is-referenced-by-count":2,"title":["Data on the Impact of Epidemic on Nursing Staff\u2019s Mental Health in the Context of Wireless Network"],"prefix":"10.1155","volume":"2022","author":[{"given":"Dan","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Operating Room, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570208, Hainan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Haikou Administrative Center Outpatient, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570208, Hainan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3903-5342","authenticated-orcid":true,"given":"YanJi","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Health Medicine, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570208, Hainan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1177\/0020764020915212"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17217857"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1708\/3382.33569"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.24457.1"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0231924"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/S2215-0366(20)30047-X"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.12740\/pp\/120373"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2020.113264"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1111\/pcn.12988"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.3201\/eid2607.200407"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17228479"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.24869\/psyd.2020.251"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2020.113003"},{"issue":"2","key":"14","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S2215-0366(21)00005-5","article-title":"COVID-19 and mental health","volume":"8","author":"The Lancet Psychiatry","year":"2021","journal-title":"The Lancet Psychiatry"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291719000151"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2019.112732"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.3390\/ijms21030969"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.11.040"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00798"},{"issue":"1","key":"20","doi-asserted-by":"crossref","first-page":"142","DOI":"10.3390\/s19010142","article-title":"PPSDT: a novel privacy-preserving single decision tree algorithm for clinical decision-support systems using IoT devices","volume":"19","author":"A. 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