Validation Française de l’Échelle de la Personnalité Temporelle (French Validation of the Time Personality Indicator)

RÉSUMÉ Certains auteurs ont développé un intérêt pour la compréhension des aptitudes associées à la gestion du temps. Ainsi, plusieurs définitions théoriques ont été proposées afin de mieux cerner ce concept et une multitude de questionnaires a été développée afin de le mesurer. La présente étude visait à valider la traduction française d’un de ces outils, soit le Time Personality Indicator (TPI). Des analyses exploratoires et confirmatoires ont été effectuées sur l’ensemble des données recueillies auprès de 1 267 étudiants et employés de l’Université Laval ayant complété la version française du TPI ainsi que d’autres mesures de la personnalité. Les résultats ont révélé qu’une solution à huit facteurs permet de mieux décrire les données de l’échantillon. La discussion présente les raisons pour lesquelles la version française du TPI est valide, identifie certaines limites de la présente étude et souligne l’utilité de cet outil pour la recherche sur la gestion du temps. (ABSTRACT: Numerous authors have developed an interest towards the understanding of the abilities related to time management. As a consequence, multiple theoretical definitions have been proposed to explain time management. Likewise, several questionnaires have been developed in order to measure this concept. The aim of this study was to validate a French version of one of these tools, namely the Time Personality Indicator (TPI). The French version of the TPI and other personality questionnaires were completed by 1267 students and employees of Université Laval. The statistical approach used included exploratory and confirmatory analyses. Results revealed that an eight factor model provided a better adjustment to the data. The discussion provides arguments supporting the validity of the French version of the TPI and underlines the importance of such a tool for the research on time management)

Mapping of Submerged Aquatic Vegetation in Rivers From Very High Resolution Image Data, Using Object Based Image Analysis Combined with Expert Knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy.

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.