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Multicenter Study
. 2021 Feb 25;9(1):36.
doi: 10.1186/s40359-021-00534-5.

Predictive factors of quality of life among medical students: results from a multicentric study

Alice de Queiroz Constantino Miguel  1   2 , Patricia Tempski  3 , Renata Kobayasi  3 , Fernanda B Mayer  4 , Milton A Martins  3   5
Affiliations
Multicenter Study

Predictive factors of quality of life among medical students: results from a multicentric study

Alice de Queiroz Constantino Miguel et al. BMC Psychol. .

Abstract

Background: Medical students have a worse perception of Quality of Life (QoL) and a high prevalence of psychosocial suffering when compared to the general population. The objective of this study was to investigate associated factors with Quality of Life of Brazilian medical students from an exploratory analysis in a cross-sectional study described in accordance with the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines.

Methods: This is a cross-sectional and multicenter study with national sample randomized by sex and year of the course. Data were collected between August 2011 and August 2012, using an electronic platform (VERAS platform). Our outcomes included: personal quality of life (QoLp) and quality of life related to medical course activities (QoLmc), both measured using a score ranging from 0 (worst) to 10 (best). Variables as predictors: the World Health Organization Quality of Life Assessment abbreviated version (WHOQOL-BREF); VERAS-Q (a questionnaire created to evaluate the QoL of students in health professions); Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), Beck Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI), Maslach Burnout Inventory (MBI), Resilience Scale (RS-14), Interpersonal Reactivity Multidimensional Scale (IRMS) and Dundee Ready Education Environment Measure (DREEM).

Results: Our sample is comprised of 1350 (81.8%) medical students. When comparing predictors and both quality of life outcome measures, we found a negative correlation between QoL and the BDI, PSQI and ESS scores. Through a multiple linear regression mode we identified the median of significant predictors to higher QoL. We then run a tree regression model that demonstrated that the VERAS-Q-physical health domain (a domain assessing self-care, self-perception of health, sleep, leisure, physical activity and appearance) was the most important factor predicting QoL. Students with a VERAS-Q-physical health score ≥ 60.9 and a VERAS-Q-time management (address the management of the student's time, free time and whether he can dedicate himself to other activities besides the course) score ≥ 55.7 presented the best QoLmc (score: 8.08-9.63%). Students with a VERAS-Q-physical health score ≥ 79.7 presented the highest QoLp (score 8.93-8.74%).

Conclusion: Physical symptoms, self-perception of health and self-care assessed by the VERAS-Q physical domain had association with both final outcomes. Time management seems to have a protective role for better Quality of Life. These variables should be taken in consideration when designing interventions to improve Quality of Life among medical students.

Keywords: Education; Medical; Multicenter study; Predictive factors; Quality of life; Students; Time management.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Association between personal and medical course quality of life. Legend: QOL personal–personal Quality of Life; QOL medical course-Quality of Life related to medical course
Fig. 2
Fig. 2
Regression tree of quality of life related to medical course. Legend: QOL medical course-Quality of Life related to medical course
Fig. 3
Fig. 3
Regression tree of personal quality of life. Legend: QOL personal–personal Quality of Life
See this image and copyright information in PMC

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