Objective To explore the level and influencing factors of frontline nurses’ post-traumatic growth (PTG) during COVID-19 epidemic. Methods A cross-sectional survey was conducted in February 2020 in three hospitals in China. The Post-traumatic Growth Inventory (PTGI) was used to investigate the PTG of frontline nurses. Data on related factors, including demographic characteristics and subjective variables, were collected. The Event-Related Rumination Inventory was used to assess rumination. Pearson’s or Spearman’s correlation was calculated for bivariate analysis. Independent sample t-tests or one-way analysis of variance and multiple linear regression analysis were used to examine the related factors. Results A total of 179 frontline nurses were recruited, and 167 were included in the analyses. The mean PTG score was 70.53±17.26. The bivariate analyses showed that deliberate rumination was modestly positively correlated with PTG (r=0.557, p<0.01), while intrusive rumination had a modest negative correlation with PTG (r=−0.413, p<0.01). Multiple linear regression demonstrated that working years, self-confidence in frontline work, awareness of risk, psychological intervention or training during the epidemic and deliberate rumination were the main influencing factors of PTG among frontline nurses and accounted for 42.5% of the variance (F=31.626, p<0.001). Conclusions The PTG of frontline nurses was at a medium to high level and was influenced by working years, self-confidence in frontline work, awareness of risk, psychological intervention or training and deliberate rumination. It is necessary to strengthen psychological guidance and training for frontline nurses and promote their deliberate rumination on epidemic events to improve their PTG.

Background: Cancer is highly prevalent worldwide. Family resilience is a positive variable that helps families burdened by advanced cancer to cope effectively. This study aimed to describe the family resilience of advanced cancer patients and caregivers in dyads and identify its influencing factors at the individual and dyadic levels. Methods: This multisite cross-sectional study was conducted in oncology units in five tertiary hospitals in China. A total of 270 advanced cancer patient-caregiver dyads were recruited between June 2020 and March 2021. Patients’ and caregivers’ family resilience was measured by the Family Resilience Assessment Scale. Data on potential influencing factors, including demographic and disease-related characteristics as well as family sense of coherence, psychological resilience, perceived social support, symptom burden, and caregiver burden, were collected. Multilevel modeling analysis was adopted to control for the interdependence of the dyads. Results: A total of 241 dyads were included in the data analysis. The mean ages of patients and caregivers were 53.96 (SD 15.37) and 45.18 (SD 13.79) years, respectively. Most caregivers were spouses and adult children (45.6% and 39.0%, respectively). Patients reported a higher mean family resilience score than caregivers (152.56 vs. 149.87, respectively). Undergoing fewer than two types of treatment and a lower symptom burden of patients predicted higher patient (B = -9.702, -0.134, respectively) and caregiver (B = -5.462, -0.096, respectively) family resilience. Patients also reported higher family resilience under the following conditions: 1) were on a medical insurance plan other than the new rural cooperative medical system (B = 6.089), 2) had a better family sense of coherence (B = 0.415), 3) whose caregivers were unmarried (B = 8.618), perceived lower social support (B = -0.145) and higher psychological resilience (B = 0.313). Caregivers who were ≤ 44 years old (B = -3.221), had similar previous caregiving experience (B = 7.706), and had a stronger family sense of coherence (B = 0.391) reported higher family resilience. Conclusions: Our findings highlight the importance of adopting a dyadic approach when caring for advanced cancer patients and their caregivers. Dyadic longitudinal research is suggested to discover more modifiable factors of family resilience and tailored interventions are needed to obtain optimal dyadic outcomes.

Background: The adverse reactions (ADRs) of targeted therapy were closely associated with treatment response, clinical outcome, quality of life (QoL) of patients with cancer. However, few studies presented the correlation between ADRs of targeted therapy and treatment effects among cancer patients. This study was to explore the characteristics of ADRs with targeted therapy and the prognosis of cancer patients based on the clinical data. Methods: A retrospective secondary data analysis was conducted within an ADR data set including 2703 patients with targeted therapy from three Henan medical centers of China between January 2018 and December 2019. The significance was evaluated with chi-square test between groups with or without ADRs. Univariate and multivariate logistic regression with backward stepwise method were applied to assess the difference of pathological characteristics in patients with cancer. Using the univariate Cox regression method, the actuarial probability of overall survival was performed to compare the clinical outcomes between these two groups. Results: A total of 485 patients were enrolled in this study. Of all patients, 61.0% (n = 296) occurred ADRs including skin damage, fatigue, mucosal damage, hypertension and gastrointestinal discomfort as the top 5 complications during the target therapy. And 62.1% of ADRs were mild to moderate, more than half of the ADRs occurred within one month, 68.6% ADRs lasted more than one month. Older patients (P = 0.022) and patients with lower education level (P = 0.036), more than 2 comorbidities (P = 0.021), longer medication time (P = 0.022), drug combination (P = 0.033) and intravenous administration (P = 0.019) were more likely to have ADRs. Those with ADRs were more likely to stop taking (P = 0.000), change (P = 0.000), adjust (P = 0.000), or not take the medicine on time (P = 0.000). The number of patients with recurrence (P = 0.000) and metastasis (P = 0.006) were statistically significant difference between ADRs and non-ADRs group. And the patients were significantly poor prognosis in ADRs groups compared with non-ADRs group. Conclusion: The high incidence of ADRs would affect the treatment and prognosis of patients with cancer. We should pay more attention to these ADRs and develop effective management strategies.

Background: Return to work following myocardial infarction (MI) represents an important indicator of recovery. However, MI can cause patients to feel pressure, loneliness and inferiority during work and even detachment from employment after returning to work, which may affect their quality of life. The aims of this study were to identify the influencing factors of Health-related quality of life (HRQoL) in patients with MI after returning to work and explore the correlations between these factors and HRQoL. Method: This was a cross-sectional study. All participants were recruited from tertiary hospitals in China from October 2017 to March 2018. The general data questionnaire, Short-Form Health Survey-8 (SF-8), Health Promoting Lifestyle ProfileII (HPLPII), Medical Coping Modes Questionnaire (MCMQ) and Social Supporting Rating Scale (SSRS) were used to assess 326 patients with myocardial infarction returned to work after discharge. Multiple linear regression analysis was performed to explore factors related to HRQoL in patients with MI after returning to work. Results: The sample consisted of 326 patients. The mean total score of quality of life was 28.03 ± 2.554. According to the multiple linear regression analysis, next factors were associated with better HRQoL: younger age (B = − 0.354, P = 0.039), higher income (B = 0.513, P = 0.000), less co-morbidity (B = − 0.440, P = 0.000), the longer time taken to return to work (B = 0.235, P = 0.003), fewer stents installed (B = − 0.359, P = 0.003), participation in cardiac rehabilitation (CR) (B = − 1.777, P = 0.000), complete CR (B = − 1.409, P = 0.000), better health behaviors such as more health responsibility (B = 0.172, P = 0.000) and exercise (B = 0.165, P = 0.000), better nutrition (B = 0.178, P = 0.000) and self-realization (B = 0.165, P = 0.000), stress response (B = 0.172, P = 0.000), more social support such as more objective support (B = 0.175, P = 0.000), subjective support (B = 0.167, P = 0.000) and better utilization of social support (B = 0.189, P = 0.028), positive copping strategies such as more coping (B = 0.133, P = 0.000) and less yield (B = − 0.165, P = 0.000). Conclusions: HRQoL of MI patients after returning to work is not satisfactory. Health behavior, coping strategies, social support are factors which can affect HRQoL. A comprehensive and targeted guide may be a way to improve HRQoL and to assist patients’ successful return to society.