Purpose: This study examines quality of life of medical students in Yemen by evaluating validity and reliability of the World Health Organization Quality of Life questionnaire (WHOQOL) and assessing potential influencing factors. Methods: This is a single-centered cross-sectional study conducted in Hadramout University College of Medicine, Mukalla, Yemen during the academic year of 2019. The WHOQOL questionnaire was distributed among medical students. For validity, item discriminate validity and confirmatory factor analysis were assessed and for reliability, Cronbach's α test was examined. Independent sample t -test and one-way Analysis of Variance (ANOVA) were used to examine the academic level, gender, academic performance, and basic life necessities including water, electricity supply, sewage treatment and type of residence. Results: A total of 495 medical students have responded to this questionnaire which has demonstrated an adequate validity and good reliability. The mean score for students' self-rating of their quality of life in the major domains was found to be in a descending order (Mean ± SD): psychological health (55.18 ± 17.84), environmental (52.14 + 17.60), physical health (48.15 + 14.73) and social relations (45.09 ± 20.81). Demographics and basic life needs exhibit relationship with Quality of Life among medical students. Conclusion: The WHOQOL-BREF is a valid and reliable tool among medical students in Hadramout University. Demographics and basic life needs seem to impact Yemeni medical students' Quality of Life. Wellness and mentoring programs should be considered to ameliorate effects related to deteriorating medical students' Quality of Life in Hadramout University.

IntroductionMedical students often adopt different learning strategies and motives that guide them during studying. Preclinical students often face difficulties coping with new learning environments hence impacting their learning styles. The literature shows that studies examining the influence of surface and deep learning approaches on the academic performance of preclinical students are limited. Hence, this study aims to measure the impact of superficial and deep learning approaches on their academic performance.MethodsThe revised two‐factor version of the Study Process Questionnaire (R‐SPQ‐2F) was distributed among first, second and third year medical students at Alfaisal University. Each learning approach, surface or deep, is composed of motives and strategies. Exploratory factor analysis was conducted to validate the tool and Cronbach's alpha to assess its reliability. GPA was grouped into four groups: excellent (>3.5), good (3.0–3.5), average (2.5 – 2.9) and poor (<2.5). Regression analysis explored the prediction of academic performance by different learning styles. One‐way Analysis of Variance (ANOVA) and independent samples t‐test were used to examine the differences between learning styles, different study resources and average study time.ResultsA total of 159 students were enrolled in the study and the tool was tested to be valid and reliable. Deep learning approach predicted higher academic performance among the students (β 0.26, p=0.001). Excellent students showed higher ratings of deep learning strategies than good (p=0.025) and average students (p=0.001). Students who study for more than 8 hours daily also ranked higher in deep learning strategies than those who study for 4–6 hours (0.024) and 2–4 hours (p=0.01). Additionally, students who read books demonstrated higher self‐rating in deep motives and strategies (p=0.008 & p=0.005, respectively). Those who watch videos and utilize internet to expand medical knowledge tend to have higher ratings in deep motives with p values of 0.038 and 0.019, respectively.ConclusionThis study demonstrates adequate validity and reliability of R‐SPQ‐2F model among the study subjects. Students with deep approach style tend to study for a longer time, use different resources and achieve a higher GPA compared to students with surface approach. This may help students with surface approach shift their learning motivations to deep motives in order to achieve a transition to deep learning approach.