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Background: In December 2019, early cases of COVID-19 were identified in Wuhan, China. By late January 2020, it was evident that COVID-19 was rapidly spreading and represented a national health emergency. In order to contain the spread of COVID-19, China adopted a centralized treatment plan by appointing designated hospitals in each region. Shantou Central Hospital is a Grade A Class A general hospital in Guangdong Province. It was appointed as a provincial COVID-19 designated treatment hospital on January 21, 2020, to provide all COVID-19-related treatments for the city of Shantou. The nursing department at Shantou Central Hospital is fully responsible for hospital nursing administration, nursing human resource management, nursing quality management, and all nursing tasks related to hospital medical care, nursing, teaching, scientific research, preventive healthcare, and so on. Objective: To summarize the role of nursing management in transforming a general hospital into a designated hospital for treatment of COVID-19 patients. Methods: We undertook a series of nursing management measures in the strategic phase and the implementation phase. Findings: Through a series of nursing management measures, all COVID-19 patients admitted to our hospital were cured and discharged. All non-COVID-19 patients and staff hospitalized during the same period were not infected with the virus. During this period, our hospital completed 7,466 operations. Hence, our nursing management measures were effective. Conclusions: Our efficient nursing management system, first of all, effectively mobilized all available manpower; secondly, up-skilled and trained personnel within a very short period of time; thirdly, provided reliable logistical support for front-line protection equipments; and finally, motivated nurses during this very difficult time to make a significant positive contribution to the fight against COVID-19 pandemic.

Background: Parotid tumours (PTs) have a variety of pathological types, and the surgical procedures differ depending on the tumour type. However, accurate diagnosis of PTs from the current preoperative examinations is unsatisfactory. Methods: This retrospective study was approved by the Ethics Committee of our hospital, and the requirement for informed consent was waived. A total of 73 patients with PTs, including 55 benign and 18 malignant tumours confirmed by surgical pathology, were enrolled. All patients underwent diffusion-weighted imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), susceptibility-weighted imaging (SWI), T2weighted imaging (T2WI), and T1-weighted imaging (T1WI). The signal uniformity and capsule on T2WI, apparent diffusion coefficient (ADC) derived from DWI, semi-quantitative parameter time-intensity curve (TIC) pattern, and quantitative parameters including transfer constant (K trans ), extravascular extracellular volume fraction (V e ), wash-out constant (K ep ) calculated from DCE-MRI, and intratumoural susceptibility signal (ITSS) obtained from SWI were assessed and compared between benign and malignant PTs. Logistic regression analysis was used to select the predictive parameters for the classification of benign and malignant parotid gland tumours, and receiver operating characteristic (ROC) curve analysis was used to evaluate their diagnostic performance. Results: Malignant PTs tended to exhibit a type C TIC pattern, whereas benign tumours tended to be type A and B (p < 0.001). Benign PTs had less ITSS than malignant tumours (p < 0.001). Multivariate analyses showed that ADC, V e , and ITSS were predictors of tumour classification. ROC analysis showed that the area under the curve (AUC) of ADC, V e , ITSS, and ADC combined with V e were 0.623, 0.615, 0.826, and 0.782, respectively, in differentiating between malignant and benign PTs. When ITSS was added, the AUCs of ADC, V e , and ADC combined with V e increased to 0.882, 0.848, and 0.930, respectively. Conclusion: SWI offers incremental diagnostic value to DWI and DCE-MRI in the characterisation of parotid gland tumours.; PTs, Parotid tumours; CT, computed tomography; MRI, magnetic resonance imaging; DWI, diffusion-weighted imaging; DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; ADC, apparent diffusion coefficient; WTs, Warthin tumours; TIC, time-intensity curve; EES, extracellular extravascular space; K trans , volume transfer constant between blood plasma and EES; V e , EES fractional volume; K ep , flux rate constant between the EES and plasma; SWI, susceptibility-weighted imaging; ITSS, intratumoural susceptibility signal; SE-T1WI, spin-echo T1-weighted imaging; SE-T2WI, spin-echo T2-weighted imaging; VIBE, volume interpolated body examination; ROIs, regions of interest; ICC, intra-class correlation coefficient; ROC, receiver operating characteristic; AUC, area under the curve.

PurposeStigma is common in patients with breast cancer after surgery, which has a negative impact on the quality of life (QOL). This study aimed to investigate the QOL of breast cancer patients after surgery and to analyze the multiple chains mediating effects of self-disclosure and social support between stigma and QOL.MethodsA total 292 patients of breast cancer patients after operation were recruited in this study. A questionnaire survey was conducted using the general information questionnaire, the consumer experiences of stigma questionnaire (CESQ), the distress disclosure index(DDI), the perceived social support scale(PSSS), and the functional assessment of cancer therapy-breast(FACT-B). Path analysis was conducted to test the hypothesized serial multiple mediation model.ResultsThe total scores of stigma, self-disclosure, social support and QOL were 15 (10 ~ 22), 39 (31 ~ 46), 58 (50 ~ 67) and 88 (74 ~ 104) respectively. QOL of breast cancer patients after the operation was negatively correlated with stigma (p &lt; 0.01), and positively correlated with self-disclosure and social support (p &lt; 0.01). Self-disclosure and social support played a complete mediating effect between stigma and QOL, and the total mediating effect value was 85. 87%.ConclusionsSelf-disclosure and social support play a complete intermediary role between stigma and QOL. In order to improve the quality of life of patients, medical staff should pay attention to the assessment of stigma, encourage patients to express their emotions, and encourage their families and friends to respond to their expression and needs of patients.

Background We aim to create a holistic competency-based assessment system to measure competency evolution over time – one of the first such systems in China. Method Two rounds of self-reported surveys were fielded among the graduates from the Shantou University Medical College: June through December 2017, and May through August 2018. Responses from three cohorts of graduates specializing in clinical medicine – new graduates, resident physicians, and senior physicians – were analyzed. Gaps between respondents’ expected and existing levels of competencies were examined using a modified service quality model, SERVQUAL Results A total of 605 questionnaires were collected in 2017 for the construction of competency indicators and a 5-level proficiency rating scale, and 407 in 2018, for confirmatory factor and competency gap analysis. Reliability coefficients of all competency indicators (36) were greater than 0.9. Three competency domains were identified through exploratory factor analysis: knowledge (K), skills (S), and attitude (A). The confirmatory factor analysis confirmed the fit of the scale (CMIN/DF < 4; CFI > 0.9; IFI > 0.9; RMSEA ≤ 0.08). Within the cohorts of resident and senior physicians, the largest competency gap was seen in the domain of knowledge (K): −1.84 and −1.41, respectively. Among new graduates, the largest gap was found in the domain of skills (S) (−1.92), with the gap in knowledge (−1.91) trailing closely behind. Conclusions A competency-based assessment system is proposed to evaluate clinician’s competency development in three domains: knowledge (K), skills (S), and attitude (A). The system consists of 36 competency indicators, a rating scale of 5 proficiency levels, and a gap analysis to measure competency evolution through 3 key milestones in clinician’s professional career: new graduate, resident physician, and senior physician. The competency gaps identified can provide evidence-based guide to clinicians’ own continuous development as well as future medical curriculum improvements.