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- Lindqvist, Christina, 1975-
(author)
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L'influence translinguistique dans l'interlangue française : Étude de la production orale d'apprenants plurilingues
- 2006
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Doctoral thesis (other academic/artistic)abstract
- The present study concerns cross-linguistic influence in the spoken French of multilingual learners. The main purpose is to investigate to what degree, and in what manner, previously acquired languages (L1, L2(s)) influence the target language, L3. Given the fact that the study only concerns spoken interlanguage, it makes use of a psycholinguistic perspective, which takes models of oral production into account. The analysis is divided into two main parts. The first concerns the oral production of 30 Swedish learners of French, who fall into three groups according to their previous exposure to French: beginners, secondary school students and university students. The results show that proficiency in the L3 is crucial in at least two ways. First, there is a correlation between the level of proficiency in the L3 and the number of instances of cross-linguistic influence in that the least advanced learners produce the highest number of cross-linguistic lexemes, whereas the most advanced learners produce the lowest number. Second, the level of proficiency in the L3 is decisive for the number of background languages (L1, L2) used during oral production in L3: the lower the proficiency in the L3, the more background languages are used, and vice versa. The second part of the analysis contains six case studies of learners with partly different L1s and L2s. It focuses on the roles of the background languages during conversation in L3 and on the factors contributing to the attribution of these roles. The results point at both similarities and differences between the learners with respect to the roles of the background languages. A result common to all the learners is the use of Swedish L1/L2 and English L1 as an instrumental language, i.e. a language used rather strategically with a communicative purpose. The use of these languages in this function seems to be due to the fact that Swedish and English are shared languages between the learner and the interlocutor.
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| 2. |
- Singleton, Rosie K., et al.
(author)
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Risk prediction for renal cell Carcinoma : Results from the European Prospective Investigation into Cancer and nutrition (EPIC) prospective cohort study
- 2021
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In: Cancer Epidemiology Biomarkers and Prevention. - : AACR. - 1055-9965 .- 1538-7755. ; 30:3, s. 507-512
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Journal article (peer-reviewed)abstract
- Background: Early detection of renal cell carcinoma (RCC) has the potential to improve disease outcomes. No screening program for sporadic RCC is in place. Given relatively low incidence, screening would need to focus on people at high risk of clinically meaningful disease so as to limit overdiagnosis and screen-detected false positives. Methods: Among 192,172 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (including 588 incident RCC cases), we evaluated a published RCC risk prediction model (including age, sex, BMI, and smoking status) in terms of discrimination (C-statistic) and calibration (observed probability as a function of predicted probability). We used a flexible parametric survival model to develop an expanded model including age, sex, BMI, and smoking status, with the addition of self-reported history of hypertension and measured blood pressure. Results: The previously published model yielded well-calibrated probabilities and good discrimination (C-statistic [95% CI]: 0.699 [0.679–0.721]). Our model had slightly improved discrimination (0.714 [0.694–0.735], bootstrap optimism-corrected C-statistic: 0.709). Despite this good performance, predicted risk was low for the vast majority of participants, with 70% of participants having 10-year risk less than 0.0025. Conclusions: Although the models performed well for the prediction of incident RCC, they are currently insufficiently powerful to identify individuals at substantial risk of RCC in a general population. Impact: Despite the promising performance of the EPIC RCC risk prediction model, further development of the model, possibly including biomarkers of risk, is required to enable risk stratification of RCC.
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