Patient case: 14-year-old female with Li-Fraumeni syndrome; genetic feature: TP53

Clinical trial: The Effects of "Three Good Things" Positive Psychotherapy on Nurses' Burnout

Summary: A randomized, controlled trial was conducted for 73 Chineses nurses from The Second Xiangya Hospitcal of Central South University (33 in the experimental group, 40 in the control group). The experimental group received a six-month Wechat-based "three good things" positive psychotherapy from August 2015 to January 2016, while the control group only received normal psychological instruction from the hospital. A socio-demographic sheet, Malsach Burnout Inventory-General Survey, the Turnover Intention Scale, The Job Satisfaction Scale, The Job Performance Scale, General Self-efficacy Scale, The Trait Coping Style Scale (TCSS), The Connor-Davidson Resilience Scale (CD-RISC) were used to collect data prior to and immdediately after the intervention. The blood cortisol was also evaluated prior to and immdediately after the intervention. SPSS 23.0 was used for data analysis. Descriptive statistics, ANOVA, Chi-square test, repeated-measures analysis and T-test were employed to analyse the effect of "three good things" intervention on nurse burnout. We hypothesis that the "three good things" positive psychotherapy could alleviate nurses' burnout, turnover intention, improve their job performance, job satisfaction, self-efficacy, resilience, introduce nurses' to use positive coping strategies to overcome adversities. Moreover, their blood cortisol would be reduced after the intervention.

Detailed description: Study design and sample In the present study, we used a randomized, controlled design to assess the effect of "three good things" positive psychotherapy from August 2015 to January 2016. Measures were administered before (T0) and immediately after (T1) the intervention.
  The study sample were nurses recruited from one three-level general hospital in Changsha, Hunan, China. The sample size calculation was conducted via PASS statistical software (NCSS LCC, East Kaysville, UT, USA) . The effect size was 0.67, power was 0.80, and margin of error type Ⅰ was 0.05. Accordingly, the sample size was 64. Stochastic tables' law was used for group division. A total of 193 nurses completed the MBI-GS, and 102 nurses who met the inclusion criteria were randomly selected for the study. However, only 73 completed the study, with 33 in the experimental group and 40 in the control group.
  Instruments A socio-demographic questionnaire, the Maslach Burnout Inventory-General Survey (MBI-GS), the turnover intention questionnaire, the Job Performance scale, the Job Satisfaction scale, the General self-efficacy Scale, the CD-RISC and the Trait Coping Style Scale were used to collect the data. The blood cortisol was also collected. The detail instruction of these scales could be found at Outcome Measures section.
  Ethical consideration This study was approval by The Institutional Review Board (IRB) of Xiangya Nursing School, Central South University. Participants were informed about the objectives and procedures of the study before they began the survey. All data were held confidential. Only the research team could access the data.
  Data analysis Data analysis was performed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA). Descriptive statistics was used to describe demographic data, burnout, turnover intention, job performance, job satisfaction, self-efficacy, resilience, coping style and cortisol. Generalized repeated-measures analysis of variance (ANOVA) was used to demonstrate the effect of intervention and time-intervention interaction.

Eligibility: Inclusion Criteria:
  -  registered nurses or licenced practical nurses
  -  who provided direct care to residents
  -  who's MBI-GS score were no less than 1.5
  -  who didn't take any hormone therapy
  -  were Chinese speakers.
  Exclusion Criteria:
  -  student nurses
  -  who suffered from diseases that influence their hormone levels
  -  who participated similar studies
  -  who had no interest in this study.

Relevance to patient case: not relevant