RESUMEN
OBJECTIVES: The objective of this study was to develop and validate the Knowledge of Suicide Scale (KSS), elaborated to assess adherence to myths about suicide. METHODS: The KSS is a self-questionnaire including 22 statements relating to myths about suicide for which the respondent is asked to rate his degree of adherence on a scale ranging from 0 ("strongly disagree") to 10 ("completely agree"). Using the script concordance test scoring method, the respondents' scores were compared with those of experts to obtain, for each item, a score between 0 (maximum deviation with the experts) and 1 (minimum deviation with the experts). One thousand and thirty-five individuals (222 psychiatric interns, 332 medical interns in the first semester excluding psychiatry and 481 journalism students) were included. RESULTS: According to the exploratory factor analysis, the KSS is a two-dimensional scale: the first subscale includes 15 items and the second seven items. The tool showed excellent face validity, correct convergent and divergent validities (multi-method multi-feature analyzes), and good internal consistency (Cronbach's alpha coefficient between 0.66 and 0.83 for scales and subscales). The KSS is moderately and negatively correlated with the Stigma of Suicide Scale (r=-0.3). It significantly discriminates groups with different expected levels of knowledge regarding suicide (P<0.001). CONCLUSIONS: The KSS demonstrated good psychometric properties to measure adherence to myths about suicide. This tool could be useful in assessing the effectiveness of suicide prevention literacy improvement programs.
RESUMEN
INTRODUCTION: Since the late 1990s, research and administrative institutions have been developing health data warehouses and increasingly reusing claims data. The impact of these changes is not yet completely quantified. Our objective was to compare the change in the number of patients included per study between observational and interventional studies over a 20-year period starting in 1995. MATERIALS AND METHODS: We extracted all abstracts from studies published in three leading medical journals over the period 1995-2014 (18,107 studies). Then, we divided our study into two steps. First, we constructed an SVM-based predictive model to categorize each abstract into "observational", "interventional" or "other" studies. In a second step, we built an algorithm based on regular expressions to automatically extract the number of included patients. RESULTS: During the investigated period, the median number of enrolled patients per study increased for interventional studies, from 282 in 1995-1999 to 629 in 2010-2014. In the same time, the median number of patients increased more for observational studies, from 368 in 1995-1999 to 2078 in 2010-2014. DISCUSSION: The routine storage of an increasing amount of data (from data warehouses or claims data) has had an impact in recent years on the number of patients included in observational studies. The recent development of "randomized registry trials" combining, on the one hand, an intervention and, on the other hand, the identification of the outcome through data reuse, may also have an impact, over the next decade, on the number of patients included in randomized clinical trials.