Translation and Validation of a Chinese Version of the Medical Professionals’ Resilience Scale
Main Article Content
Abstract
Due to its strong psychometric properties, the Medical Professionals Resilience Scale (MeRS) has shown promising results in measuring the resilience of medical professionals. However, a validated Chinese version of the MeRS has yet to be developed. This study aimed to assess the reliability and validity of the Chinese-translated version of the MeRS (MeRS-C) among medical students in Yancheng City, China. The study involved 782 medical student volunteers from Jiangsu Vocational College of Medicine, aged between 19 and 23. The meRS-C comprises 37 items categorised into four factors and was meticulously translated and adapted into Chinese. An exploratory factor analysis (EFA) was conducted on data from Group 1 (n = 391) to evaluate the factor structure, and a second-order confirmatory factor analysis (CFA) was conducted on data from Group 2 (n = 391) to assess model fit. Before being administered to participants, the questionnaire was reviewed by experts and pre-tested. The results of the EFA supported the validity of the MeRS-C, explaining 71.431% of the total variance, with factor loadings ranging from 0.775 to 0.892. The CFA displayed favourable model fit indices (CMICMIN/DF = 1.756; Tucker-Lewis Index (TLI) = 0.96; Standardized Root Mean Square Residual (SRMR) = 0.037; Comparative Fit Index (CFI) = 0.963; and Root Mean Square Error of Approximation (RMSEA) = 0.044) and demonstrated good convergent and discriminant validity. The questionnaire exhibited high internal consistency, with reliability values ranging from 0.897 to 0.967. Thus, the concise Chinese version of the MeRS showed good reliability and validity, making it suitable for identifying sources of resilience in Chinese healthcare trainees.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Dyrbye LN, Massie Jr FS, Eacker A, Harper W, Power D, Durning SJ, et al. Relationship between burnout and professional conduct and attitudes among US medical students. JAMA. 2010;304(11):1173–80. https://doi.org/10.1001/jama.2010.1318
Kuan G, Abdullah N, Kueh YC, Ibrahim MI, Shafei MN. Co-curricular activities and motives for participating in physical activity among health science students at Universiti Sains Malaysia. Malays J Med Sci. 2019;26(1):138–46. https://doi.org/10.21315/mjms2019.26.1.13
Rotenstein LS, Ramos MA, Torre M, Segal JB, Peluso MJ, Guille C, et al. Prevalence of depression, depressive symptoms, and suicidal ideation among medical students: a systematic review and meta-analysis. JAMA. 2016;316(21):2214–36. https://doi.org/10.1001/jama.2016.17324
Dyrbye LN, Thomas MR, Shanafelt TD. Medical student distress: causes, consequences, and proposed solutions. Mayo Clin Proc. 2005;80(12):1613–22. https://doi.org/10.4065/80.12.1613
Haglund MEM, aan het Rot M, Cooper NS, Nestadt PS, Muller D, Southwick SM, et al. Resilience in the third year of medical school: a prospective study of the associations between stressful events occurring during clinical rotations and student well-being. Acad Med. 2009;84(2):258–68. https://doi.org/10.1097/ACM.0b013e31819381b1
Ting LK, Kuan G, Ariffin WN, Cheng KY. Psychometric properties of the self-efficacy scale among undergraduate students in Malaysia. Malays J Med Sci.2019;26(3):119–28. https://doi.org/10.21315/mjms2019.26.3.10
Taku K. Relationships among perceived psychological growth, resilience and burnout in physicians. Pers Individ Dif. 2014;59:120–3. https://doi.org/10.1016/j.paid.2013.11.003
Liew GC, Kuan G, Chin N-S, Hashim H. Mental toughness in sport: systematic review and future. Ger J Exerc Sport Res. 2019:1–14. https://doi.org/10.1007/s12662-019-00603-3
Mills J, McKimm J. Resilience: why it matters and how doctors can improve it. Br J Hosp Med. 2016;77(11):630–3. https://doi.org/10.12968/hmed.2016.77.11.630
Profit J, Sharek PJ, Amspoker AB, Kowalkowski MA, Nisbet CC, Thomas EJ, et al. Burnout in the NICU setting and its relation to safety culture. BMJ Qual Saf. 2014;23(10):806–13. https://doi.org/10.1136/bmjqs-2014-002831
Runyan C, Savageau JA, Potts S, Weinreb L. Impact of a family medicine resident wellness curriculum: a feasibility study. Med Educ Online.2016;21(1):30648. https://doi.org/10.3402/meo.v21.30648
Rotenstein LS, Torre M, Ramos MA, Rosales RC, Guille C, Sen S, et al. Prevalence of burnout among physicians: a systematic review. JAMA.2018;320(11):1131–50. https://doi.org/10.1001/jama.2018.12777
Rahman MA, Yusoff MSB, Roslan NS, Mohammad JA-M, Ahmad A. Development and validation of the medical professionals resilience scale. BMC Health Serv Res. 2021;21:482. https://doi.org/10.1186/s12913-021-06542-w
Sohail M, Yusoff MSB, Hadie SNHB, Othman AB, Rauf H. Psychometric properties of Medical Professionals Resilience Scale (MERS) for medical doctors in Pakistan. Res Square. 2022;1–13. https://doi.org/10.21203/rs.3.rs-1329420/v1
World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191–4. https://doi.org/10.1001/jama.2013.281053
Chai S, Kueh YC, Yaacob NM, Kuan G. Psychometric properties of the Malay version goal content for exercise questionnaire among undergraduate students in health campus, Universiti Sains Malaysia. Malays J Med Sci.2019;26(1):115–24. https://doi.org/10.21315/mjms2019.26.1.11
Yao L, Zhou K, Zhou Y, Kueh YC, Liu H, Liu Z, et al. Psychometric properties of the Chinese version of the exercise self-efficacy scale for the transtheoretical model: a confirmatory analysis among Chinese children and adolescents. BMC Public Health. 2024;24(1):1–11. https://doi.org/10.1186/s12889-023-17596-2
Gao C, Shi D, Maydeu-Olivares A. Estimating the maximum likelihood root mean square error of approximation (RMSEA) with non-normal data: a Monte-Carlo study. Struct Equ Model: Multidiscip J. 2020;27(2):192–201. https://doi.org/10.1080/10705511.2019.1637741
Kline RB. Principles and practice of structural equation modeling, 4th ed. New York, NY: Guilford Press; 2016.
Zhong X, Yuan KH. Bias and efficiency in structural equation modelling: maximum likelihood versus robust methods. Multivariate Behav Res. 2011;46(2):229–65. https://doi.org/10.1080/00273171.2011.558736
Shi D, DiStefano C, Zheng X, Liu R, Jiang Z. Fitting latent growth models with small sample sizes and non-normal missing data. Int J Behav Dev. 2021;45(2):179–92. https://doi.org/10.1177/0165025420979365
DeVon HA, Block ME, Moyle-Wright P, Ernst DM, Hayden SJ, Lazzara DJ, et al. A psychometric toolbox for testing validity and reliability. J Nurs Scholarsh. 2007;39(2):155–64. https://doi.org/10.1111/j.1547-5069.2007.00161.x
Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. Boston, MA: Cengage; 2019.
Wang J, Wang X. Structural equation modeling: applications using Mplus. Hoboken, NJ: John Wiley & Sons; 2019.
Lance CE, Butts MM, Michels LC. The sources of four commonly reported cutoff criteria: what did they really say? Organ Res Methods. 2006;9(2):202–20. https://doi.org/10.1177/1094428105284919
Falk CF, Savalei V. The relationship between unstandardized and standardized alpha, true reliability, and the underlying measurement model. J Pers Assess. 2011;93(5):445–53. https://doi.org/10.1080/00223891.2011.594129
Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50. https://doi.org/10.2307/3151312
Brown TA. Confirmatory factor analysis for applied research. New York, NY: Guilford Press; 2006.
Ishak W, Nikravesh R, Lederer S, Perry R, Ogunyemi D, Bernstein C. Burnout in medical students: a systematic review. Clin Teach. 2013;10(4):242–5. https://doi.org/10.1111/tct.12014
Ishak WW, Lederer S, Mandili C, Nikravesh R, Seligman L, Vasa M, et al. Burnout during residency training: a literature review. J Grad Med Educ. 2009;1(2):236–42. https://doi.org/10.4300/JGME-D-09-00054.1
Benbassat J, Baumal R, Chan S, Nirel N. Sources of distress during medical training and clinical practice: suggestions for reducing their impact. Med Teach. 2011;33(6):486–90. https://doi.org/10.3109/0142159X.2010.531156
Hojat M, Vergare MJ, Maxwell K, Brainard G, Herrine SK, Isenberg GA, et al. The devil is in the third year: a longitudinal study of erosion of empathy in medical school. Acad Med. 2009;84(9):1182–91. https://doi.org/10.1097/ACM.0b013e3181b17e55
Farquhar J, Kamei R, Vidyarthi A. Strategies for enhancing medical student resilience: student and faculty member perspectives. Int J Med Educ. 2018;9:1–6. https://doi.org/10.5116/ijme.5a46.1ccc
Thompson G, McBride RB, Hosford CC, Halaas G. Resilience among medical students: the role of coping style and social support. Teach Learn Med. 2016;28(2):174–82. https://doi.org/10.1080/10401334.2016.1146611
Bacchi, S., Licinio, J. Resilience and psychological distress in psychology and medical students. Acad Psychiatry. 2017;41(2):185–8. https://doi.org/10.1007/s40596-016-0488-0
Extremera MO, Moreno EO, González MC, Ortega FZ, Ruz RP. Validation of resilience scale (CD-RISC) in elite athletes through a structural equation model. Retos. 2017;32:96–100. https://doi.org/10.47197/retos.v0i32.49910
Furstova J, Kascakova N, Solcova IP, Hasto J, Tavel P. How Czecho-Slovakia bounces back: population-based validation of the brief resilience scale in two central European countries. Psychol Rep. 2022;125(5):2807–27. https://doi.org/10.1177/00332941211029619
Trigueros R, Magaz-González AM, García-Tascón M, Alias A, Aguilar-Parra JM. Validation and adaptation of the academic-resilience scale in the Spanish context. Int J Environ Res Public Health. 2020;17(11):3779. https://doi.org/10.3390/ijerph17113779