ABSTRACT
Objective: The central issue of this research is to evaluate the extent of cognitive appraisal and coping processes within the pandemic encounter and determines their influence on frontline healthcare providers who had been dispatched to the coronavirus disease 2019 (COVID-19) epicenter (HPDE) distress symptoms. Materials and methods: An electronic survey of the HPDE and frontline healthcare providers who worked in their original medical facility (HPOF) was conducted from March 1 to 15, 2020. Two variables, appraisal (measured with an 18-item questionnaire) and coping (measured The Brief Cope questionnaire), were used in the analysis to explain distress symptoms (Impact of Event Scale-Revised). Results: A total of 723 eligible respondents completed the survey with a response rate of 57.3% (351 HPDE and 372 HPOF). The mean IES-R scores of HPDE respondents were 26.47 ± 11.7. Of HPDE respondents, 246 (70.09%) reported distress symptoms (score 9-88). The scores of intrusion, avoidance, and hyperarousal for HPDE were 10.28 ± 4.7, 8.97 ± 4.3, and 7.20 ± 3.2, respectively. The respondents had higher scores in overall distress and three subscales than HPOF. Appraisal and coping variables explained 77% of the distress variance. Five appraisal variables (health of self, health of family/others, virus spread, vulnerability or loss of control, and general health) were positively associated with distress symptoms. Four coping variables (active coping, positive reframing, self-distraction, and behavioral disengagement) were negatively associated with distress level, whereas self-blame was positively associated with distress symptoms. Regarding the appraisal, the scores of HPDE were significantly higher than HPOF (all p-values < 0.05), whereas being isolated was not significantly different between HDPE nurses and HPOF nurses. HPDE was significantly more likely to use humor, emotional support, instrumental support, self-distractions, venting, substance use, denial, behavioral disengagement, and self-blame (P < 0.05), whereas HPOF was significantly more likely to use active coping and acceptance (P < 0.05). HPDE doctors were significantly more likely than nurses to use active coping and acceptance (P < 0.05), whereas HPDE nurses were significantly more likely to use emotional support and use self-blame (P < 0.05). Conclusion: Frontline healthcare providers who had been dispatched to the COVID-19 epicenter respondents had a higher distress level. Therefore, we should provide proactive psychological support based on specific appraisal and coping variables.
ABSTRACT
The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed tomography (CT) scans is of great importance for supervising disease progression and further clinical treatment. As labeling COVID-19 CT scans is labor-intensive and time-consuming, it is essential to develop a segmentation method based on limited labeled data to conduct this task. In this paper, we propose a self-ensembled co-training framework, which is trained by limited labeled data and large-scale unlabeled data, to automatically extract COVID lesions from CT scans. Specifically, to enrich the diversity of unsupervised information, we build a co-training framework consisting of two collaborative models, in which the two models teach each other during training by using their respective predicted pseudo-labels of unlabeled data. Moreover, to alleviate the adverse impacts of noisy pseudo-labels for each model, we propose a self-ensembling strategy to perform consistency regularization for the up-to-date predictions of unlabeled data, in which the predictions of unlabeled data are gradually ensembled via moving average at the end of every training epoch. We evaluate our framework on a COVID-19 dataset containing 103 CT scans. Experimental results show that our proposed method achieves better performance in the case of only 4 labeled CT scans compared to the state-of-the-art semi-supervised segmentation networks.