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Record W3185113789 · doi:10.1089/jpm.2021.0367

Evaluation of Symptom Distress by Edmonton Symptom Assessment System and Maslach Burnout Inventory-Medical Personnel among Medical Personnel under the Epidemic of COVID-19

2021· article· en· W3185113789 on OpenAlexaboutno aff
Zhucheng Yin, Fengming Ran, Yirui Liu, Yuan Wu, Éduardo Bruera, Qian Yu

Bibliographic record

VenueJournal of Palliative Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChinaFamily medicine

Abstract

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Journal of Palliative MedicineVol. 24, No. 10 Letters to the EditorFree AccessEvaluation of Symptom Distress by Edmonton Symptom Assessment System and Maslach Burnout Inventory-Medical Personnel among Medical Personnel under the Epidemic of COVID-19Zhucheng Yin, Fengming Ran, Yirui Liu, Yuan Wu, Huifen Wang, Eduardo Bruera, and Yu QianZhucheng YinDepartment of Thoracic Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.*Cofirst author.Search for more papers by this author, Fengming RanDepartment of Thoracic Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.*Cofirst author.Search for more papers by this author, Yirui LiuDepartment of Thoracic Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.*Cofirst author.Search for more papers by this author, Yuan WuDepartment of Radiation Oncology, and Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Search for more papers by this author, Huifen WangDepartment of Nursing, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Search for more papers by this author, Eduardo BrueraDepartment of Palliative, Rehabilitation, and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.Search for more papers by this author, and Yu QianAddress correspondence to: Yu Qian, MD, Department of Thoracic Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430070, China. E-mail Address: [email protected]Department of Thoracic Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Search for more papers by this authorPublished Online:20 Sep 2021https://doi.org/10.1089/jpm.2021.0367AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail Dear Editor:Coronavirus disease 2019 (COVID-19) outbreak has spread worldwide for over one year. In the affected countries, clinicians have faced heavy workload conditions and high risk of infection.1–4 Nowadays, COVID-19 has been under better control but clinicians working with their routine patients still need to use caution in their daily practice. Therefore, the working conditions are more complex than before the COVID-19 surge. Both physical and psychological distress of clinicians should be monitored, but there are no a simple assessment tools to measure practitioner distress. We conducted a survey to explore and compare symptom distress through the Edmonton Symptom Assessment System (ESAS)5 and burnout frequency through the Maslach Burnout Inventory-Medical Personnel (MBI) among clinicians working on the front lines (FL) of COVID-19 and their colleagues practicing in their usual wards (UWs).MethodsThe institutional review board approved the survey and protocol. As previously reported, 220 medical staff from Hubei Cancer Hospital participated in this study.3 In addition, ESAS and MBI were given to all participants. The total number of questions was 49. The participants were assured of complete anonymity. Six different methods were utilized to measure burnout. The survey was completed between March 13, 2020, and March 17, 2020.Statistical analysisWe applied standard descriptive statistics to summarize the response to all survey questions, including median, interquartile range, and range for continuous variables and frequency and proportion for categorical variables. Chi-squared test was used to assess the difference of frequencies of burnout between the FL and the UWs.ResultsA total of 190 (86%) completed the survey. Symptom distress was evaluated through ESAS (Table 1). Interestingly, participants from the UWs had a significantly higher degree of anxiety than those from the FL. Moreover, participants from the UWs also reported higher financial distress than those from the FL. The other 10 items did not differ significantly between the two groups. The burnout frequency was evaluated based on the six different criteria using MBI. The frequency of burnout was significantly lower in the FL group than in the UW group by all six criteria using MBI, as we previously reported.3Table 1. Comparison of Medical Personnel in the Front Line with Those in the Usual Cancer Ward Total (N = 190)pFront line (N = 96)Usual ward (N = 94)ESAS, median (IQR)a Pain1 (0, 1)0 (0, 2)0.27 Fatigue2 (0, 4)2 (0, 5)0.07 Nausea0 (0, 1)0 (0, 1)0.67 Depression0 (0, 3)1 (0, 4)0.07 Anxiety1 (0, 4)3 (1, 5)0.004 Drowsiness2 (1, 5)3 (1, 6)0.09 Appetite2 (0, 4)0 (0, 3)0.08 Best feeling of well being1 (0, 5)1 (0, 3)0.64 No shortness of breath0 (0, 0)0 (0, 0)0.47 Best sleep2 (1, 5)2 (1, 5)0.96 No financial distress1 (0, 3)2 (0, 5)0.02 No spiritual pain2 (1, 4)2 (1, 3)0.66SDS13 (6, 28)18 (8, 31)0.30Physical scale10 (5, 23)12 (6, 23)0.61Psychological scale2 (0, 6)5 (2, 9)0.009MBI, N (%)bMethod 1: EE >27 and DP >10 and PA ≤310 (0)11 (12)<0.05Method 2: (EE >27 or DP >10) and PA <314 (4)20 (21)<0.05Method 3: (EE >27 or DP >10 or PA <31)45 (47)72 (77)<0.05Method 4: EE >277 (7)30 (32)<0.05Method 5: Single question: “I feel burned out from my work.” (score ≥4)5 (5)22 (23)<0.05Method 6: PA <3137 (39)57 (61)<0.05aThe severity at the time of each symptom is rated from 0 to 10 on a numerical scale, with 0 indicating that a symptom is absent and 10 indicating that it is of the worst possible severity.bThe most common method of MBI has been previously reported.3DP, depersonalization; EE, emotional exhaustion; ESAS, Edmonton System Assessment Scale; IQR, interquartile range; MBI, Maslach Burnout Inventory; PA, personal achievement; SDS, symptom distress scale.DiscussionWe have further confirmed our previous finding that when comparing those working in their UWs, clinicians working on the FL had a lower frequency of burnout.3 We also found that UW staff had a lower intensity of anxiety and psychological distress on ESAS, making ESAS a potential assessment for medical staff who might face more distress after the surge of the COVID-19 crisis. ESAS is familiar to doctors and nurses and could be done within minutes. The MBI is the gold standard assessment for burnout, but it cannot reveal physical symptoms related to burnout.ConclusionESAS, with 12 items, could be a candidate tool to evaluate symptom distress especially for those medical staff when MBI, the gold standard for burnout, is unavailable.References1. Burki TK: Burnout among cancer professionals during COVID-19. Lancet Oncol 2020;21:1402. Crossref, Medline, Google Scholar2. Matsuo T, Kobayashi D, Taki F, et al.: Prevalence of health care worker burnout during the coronavirus disease 2019 (COVID-19) pandemic in Japan. JAMA Netw Open 2020;3:e2017271. Crossref, Medline, Google Scholar3. Wu Y, Wang J, Luo C, et al.: A comparison of burnout frequency among oncology physicians and nurses working on the frontline and usual wards during the COVID-19 epidemic in Wuhan, China. J Pain Symptom Manage 2020;60:e60–e65. Crossref, Medline, Google Scholar4. Zhang Y, Wang C, Pan W, et al.: Stress, burnout, and coping strategies of frontline nurses during the COVID-19 epidemic in Wuhan and Shanghai, China. Front Psychiatry 2020;11:565520. Crossref, Medline, Google Scholar5. Bruera E, Kuehn N, Miller MJ, et al.: The Edmonton Symptom Assessment System (ESAS): A simple method for the assessment of palliative care patients. J Palliat Care 1991;7:6–9. Crossref, Medline, Google ScholarFiguresReferencesRelatedDetails Volume 24Issue 10Sep 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Zhucheng Yin, Fengming Ran, Yirui Liu, Yuan Wu, Huifen Wang, Eduardo Bruera, and Yu Qian.Evaluation of Symptom Distress by Edmonton Symptom Assessment System and Maslach Burnout Inventory-Medical Personnel among Medical Personnel under the Epidemic of COVID-19.Journal of Palliative Medicine.Sep 2021.1426-1427.http://doi.org/10.1089/jpm.2021.0367Published in Volume: 24 Issue 10: September 20, 2021Online Ahead of Print:July 21, 2021PDF download

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0070.007
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.381
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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