Exploring Burnout and Mindfulness among Medical Researchers: A Global Cross-Sectional Survey
Ng, J. Y.; Syed, N.; Melendez, G.; Bilc, M. I.; Koch, A. K.; Cramer, H.
Show abstract
Burnout, a state of chronic exhaustion often characterized by feelings of emotional exhaustion, cognitive and emotional dysregulation, and psychological distancing, is an increasingly recognized issue within most professions. This syndrome results in diminished job satisfaction, strained interpersonal relationships, and decreased well-being. Socio-demographic factors have been shown to play a role in burnout risk, while trait mindfulness has been identified as an effective method to mitigate it. This study aimed to identify the prevalence of burnout risk and its relationship with mindfulness and socio-demographics among medical researchers. An anonymous, online, cross-sectional survey was administered to corresponding authors published in MEDLINE. The survey consisted of screening and socio-demographic questions, as well as validated assessment tools (i.e., shortened work-related Burnout Assessment Tool [BAT-12] and shortened Freiburg Mindfulness Inventory [FMI-14]). Responses were analysed according to the BAT and FMI guidelines, alongside regression analyses. A total of 1,732 participants completed the survey, yielding a response rate of 1.88%. Overall, 38.8% of participants were at risk or at very high risk of burnout, and the mean mindfulness score was 37.51. Multiple linear regression analysis indicated that sex, age, and employment status were significant predictors of burnout risk, while age and region significantly predicted mindfulness. Hierarchical regression analysis showed that, after controlling for socio-demographic variables, mindfulness was a strong and independent negative predictor of burnout risk. These findings on burnout risk and the influence of mindfulness and socio-demographics could guide future research in developing tailored interventions and policies that improve the well-being of medical researchers.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- High prevalence of burnout syndrome among medical and nonmedical residents during the COVID-19 pandemic 96%
- Changes in hospital staff mental health during the Covid-19 pandemic: longitudinal results from the international COPE-CORONA study 96%
- Citizenship status and career self-efficacy: An intersectional study of biomedical trainees in the United States 95%
Similar papers in this journal
- Impact of COVID-19 on College Students' at One of the Most Diverse Campuses in the United States: A Factor Analysis of Survey Data 94%
- Psychosocial impact of the Covid-19 pandemic: Identification of most vulnerable populations 94%
- Factors associated with self-reported health among New Zealand military Veterans: a cross-sectional study 94%
Similar papers in this journal
- The association between experience of COVID-19-related discrimination and psychological distress among healthcare workers for six national medical research centers in Japan 94%
- Mental health in higher-education students and non-students: evidence from a nationally representative panel study 92%
- Mental Health of Clinical Staff Working in High-Risk Epidemic and Pandemic Health Emergencies: A Rapid Review of the Evidence and Meta-Analysis 91%
Similar papers in this journal
- How should job crafting interventions be implemented to make their effects last? Study protocol of group concept mapping 94%
- Impostor Phenomenon in the Nutrition and Dietetics Profession: An Online Cross-Sectional Survey 93%
- Secondary traumatic stress and burnout in healthcare workers during COVID-19 outbreak 93%
Similar papers in this journal
- Virtual mindfulness interventions to promote well-being in young adults: A mixed-methods systematic review 94%
- Occupational stigma and post-traumatic stress disorder among healthcare workers 93%
- Closed Doors: Predictors of Stress, Anxiety, Depression, and PTSD During the Onset of COVID-19 Pandemic in Brazil 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.