Background: Learning approaches have been proposed to affect the experience of psychological stress among tertiary students in recent years. This relationship becomes important in stressful environments such as medical schools. However, the relationship between stress and learning approaches is not well understood, and often studies done cannot be generalized due to different sociocultural differences. In particular, no study in Malaysia has looked at learning approaches among medical students.
Aims: To address this gap, we examined the relationship between perceived stress and learning approaches by considering sources of stress.
Methodology: The Perceived Stress Scale (PSS-10), Medical Student Stressor Questionnaire, and the Revised Two-Factor Study Process Questionnaire were answered by the preclinical and final-year students studying MBBS in a Malaysian campus of British University.
Results: Deep learning was positively and surface learning negatively associated with perception of coping with stress. In this study, neither approaches were associated with psychological stress as opposed to previous reports. We found surface learners to report higher level of stress associated with social stressors. We found students' self-perception of feeling incompetent and feeling they need to do well to be significant sources of stress.
Discussion: Deep learning promotes psychological resilience. This is of paramount importance in learning environments where stress is highly prevalent such as medical school. Promotion of deep learning among medical students is required at earlier stages as they tend to solidify their approach through their university years and carry that approach beyond school into their workplace.
The prevalence of smokers is a major driver of lung cancer incidence in a population, though the "exposure-lag" effects are ill-defined. Here we present a multi-country ecological modelling study using a 30-year smoking prevalence history to quantify the exposure-lag response. To model the temporal dependency between smoking prevalence and lung cancer incidence, we used a distributed lag non-linear model (DLNM), controlling for gender, age group, country, outcome year, and population at risk, and presented the effects as the incidence rate ratio (IRR) and cumulative incidence rate ratio (IRRcum). The exposure-response varied by lag period, whilst the lag-response varied according to the magnitude and direction of changes in smoking prevalence in the population. For the cumulative lag-response, increments above and below the reference level was associated with an increased and decreased IRRcum respectively, with the magnitude of the effect varying across the lag period. Though caution should be exercised in interpretation of the IRR and IRRcum estimates reported herein, we hope our work constitutes a preliminary step towards providing policy makers with meaningful indicators to inform national screening programme developments. To that end, we have implemented our statistical model a shiny app and provide an example of its use.