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The validation of widely used scales facilitates the comparison across international patient samples. The objective of this study was to translate, culturally adapt and validate the Simple Shoulder Test into Brazilian Portuguese. Also we test the stability of factor analysis across different cultures.The objective of this study was to translate, culturally adapt and validate the Simple Shoulder Test into Brazilian Portuguese. Also we test the stability of factor analysis across different cultures.The Simple Shoulder Test was translated from English into Brazilian Portuguese, translated back into English, and evaluated for accuracy by an expert committee. It was then administered to 100 patients with shoulder conditions. Psychometric properties were analyzed including factor analysis, internal reliability, test-retest reliability at seven days, and construct validity in relation to the Short Form 36 health survey (SF-36).Factor analysis demonstrated a three factor solution. Cronbach's alpha was 0.82. Test-retest reliability index as measured by intra-class correlation coefficient (ICC) was 0.84. Associations were observed in the hypothesized direction with all subscales of SF-36 questionnaire.The Simple Shoulder Test translation and cultural adaptation to Brazilian-Portuguese demonstrated adequate factor structure, internal reliability, and validity, ultimately allowing for its use in the comparison with international patient samples.

BackgroundAlthough scientific innovation has been a long-standing topic of interest for historians, philosophers and cognitive scientists, few studies in biomedical research have examined from researchers' perspectives how high impact publications are developed and why they are consistently produced by a small group of researchers. Our objective was therefore to interview a group of researchers with a track record of high impact publications to explore what mechanism they believe contribute to the generation of high impact publications.Methodology/principal findingsResearchers were located in universities all over the globe and interviews were conducted by phone. All interviews were transcribed using standard qualitative methods. A Grounded Theory approach was used to code each transcript, later aggregating concept and categories into overarching explanation model. The model was then translated into a System Dynamics mathematical model to represent its structure and behavior. Five emerging themes were found in our study. First, researchers used heuristics or rules of thumb that came naturally to them. Second, these heuristics were reinforced by positive feedback from their peers and mentors. Third, good communication skills allowed researchers to provide feedback to their peers, thus closing a positive feedback loop. Fourth, researchers exhibited a number of psychological attributes such as curiosity or open-mindedness that constantly motivated them, even when faced with discouraging situations. Fifth, the system is dominated by randomness and serendipity and is far from a linear and predictable environment. Some researchers, however, took advantage of this randomness by incorporating mechanisms that would allow them to benefit from random findings. The aggregation of these themes into a policy model represented the overall expected behavior of publications and their impact achieved by high impact researchers.ConclusionsThe proposed selection mechanism provides insights that can be translated into research coaching programs as well as research policy models to optimize the introduction of high impact research at a broad scale among institutional and governmental agencies.

BackgroundThe inherent complexity of statistical methods and clinical phenomena compel researchers with diverse domains of expertise to work in interdisciplinary teams, where none of them have a complete knowledge in their counterpart's field. As a result, knowledge exchange may often be characterized by miscommunication leading to misinterpretation, ultimately resulting in errors in research and even clinical practice. Though communication has a central role in interdisciplinary collaboration and since miscommunication can have a negative impact on research processes, to the best of our knowledge, no study has yet explored how data analysis specialists and clinical researchers communicate over time.Methods/principal findingsWe conducted qualitative analysis of encounters between clinical researchers and data analysis specialists (epidemiologist, clinical epidemiologist, and data mining specialist). These encounters were recorded and systematically analyzed using a grounded theory methodology for extraction of emerging themes, followed by data triangulation and analysis of negative cases for validation. A policy analysis was then performed using a system dynamics methodology looking for potential interventions to improve this process. Four major emerging themes were found. Definitions using lay language were frequently employed as a way to bridge the language gap between the specialties. Thought experiments presented a series of "what if" situations that helped clarify how the method or information from the other field would behave, if exposed to alternative situations, ultimately aiding in explaining their main objective. Metaphors and analogies were used to translate concepts across fields, from the unfamiliar to the familiar. Prolepsis was used to anticipate study outcomes, thus helping specialists understand the current context based on an understanding of their final goal.Conclusion/significanceThe communication between clinical researchers and data analysis specialists presents multiple challenges that can lead to errors.