The paper by Schwarzbold et al.1 comments on the relatively unique population of medical students, which differs from the broader general population on key factors such as general intelligence, academic and career motivation, and self-selection to a helping profession. Thus, the data need to be understood in proper context. In general, the observed associations between the Five Factor Model of Personality (Big Five), Behavioral Inhibition and Activation Systems scales (BIS/BAS) and alcohol and marijuana use are generally consistent with the existing literature in the broader population, and consistent with findings from other countries and continents. However, some findings from this report are equivocal or do not support the broader literature regarding some specific subscales of the Big Five and BIS/BAS. Additionally, the results are partially complicated by a lack of observed association between depression and anxiety scores and the Patient Health Questionnaire (PHQ-4), which might be expected in a sufficient sample size (here, n > 700). However, the authors appropriately raise the possibility that the lack of measurement sensitivity in the screening instruments may have played a role in the observed results. Overall, then, generalizing outside this medical school population to the broader population may be premature. It might, however, suggest there are unique profiles within medical-student populations (vs. the general population), or perhaps more conservatively, within medical students in southern Brazil. The selection of the primary instruments in this study was carefully considered, as both the Big Five and BIS/BAS are well validated. Personality factors – including neuroticism and high BAS score – can indicate risk for the development of alcohol and cannabis use disorders, as well as other illicit drugs of abuse, i.e. cocaine. The authors were careful not to use causal language and to discuss their findings in terms of associations with the variables of interest, noting that the study was exploratory. The results are properly interpreted as correlational and (where appropriate) exploratory. Notably, this does not negate the value of employing sociodemographic and psychometric measures as predictive tools to aid in the examination of risky behavior patterns, both in the general population and, as in the present case, specific populations. Such inquiries can help inform the construction of screening tools in health surveys and subsequent interventions ...
Language: en
LA - en SN - 1516-4446 UR - http://dx.doi.org/10.1590/1516-4446-2020-0007 ID - ref1 ER -