The task of inertial sensor calibration has required the development of various techniques to take into account the sources of measurement error coming from such devices. The calibration of the stochastic errors of these sensors has been the focus of increasing amount of research in which the method of reference has been the so-called "Allan variance slope method" which, in addition to not having appropriate statistical properties, requires a subjective input which makes it prone to mistakes. To overcome this, recent research has started proposing "automatic" approaches where the parameters of the probabilistic models underlying the error signals are estimated by matching functions of the Allan variance or Wavelet Variance with their modelimplied counterparts. However, given the increased use of such techniques, there has been no study or clear direction for practitioners on which approach is optimal for the purpose of sensor calibration. This paper formally defines the class of estimators based on this technique and puts forward theoretical and applied results that, comparing with estimators in this class, suggest the use of the Generalized Method of Wavelet Moments as an optimal choice.

The common approach to inertial sensor calibration for navigation purposes has been to model the stochastic error signals of individual sensors independently, whether as components of a single inertial measurement unit (IMU) in different directions or arrayed in the same direction for redundancy. These signals usually have an extremely complex spectral structure that is often described using latent (or composite) models composed by a sum of underlying models which are challenging to estimate. A large amount of research in this domain has been focused on the latter aspect through the proposal of various methods that have been able to improve the estimation of these models both from a computational and a statistical point of view. However, albeit challenging, the separate calibration of the individual sensors is still unable to take into account the dependence between each of them which can have an important impact on the precision of the navigation systems. In this paper we develop a new approach to simultaneously model both the individual signals as well as the dependence between them by studying the quantity called Wavelet Cross-Covariance and using it to extend the application of the Generalized Method of Wavelet Moments to this setting. This new method can be used in many other settings for multivariate time series modelling, especially in cases where the dependence among signals may be hard to detect since it can be based on a shared underlying model that has a marginal contribution to the processes' overall variance. Moreover, in the field of inertial sensor calibration, this approach can deliver important contributions among which the possibility to test dependence between sensors, integrate their dependence within the navigation filter and construct an optimal virtual sensor that can be used to simplify and improve navigation accuracy. The advantages of this method and its usefulness for inertial sensor calibration are highlighted through a simulation study and an applied example with a small array of XSens MTi-G IMUs.

Background Medical schools can contribute to the insufficient primary care physician workforce by influencing students’ career preferences. Primary care career choice evolves between matriculation and graduation and is influenced by several individual and contextual factors. This study explored the longitudinal dynamics of primary care career intentions and the association of students’ motives for becoming doctors with these intentions in a cohort of undergraduate medical students followed over a four-year period. Methods The sample consisted of medical students from two classes recruited into a cohort study during their first academic year, and who completed a yearly survey over a four-year period from their third (end of pre-clinical curriculum) to their sixth (before graduation) academic year. Main outcome measures were students’ motives for becoming doctors (ten motives rated on a 6-point scale) and career intentions (categorized into primary care, non-primary care, and undecided). Population-level flows of career intentions were investigated descriptively. Changes in the rating of motives over time were analyzed using Wilcoxon tests. Two generalized linear mixed models were used to estimate which motives were associated with primary care career intentions. Results The sample included 217 students (60% females). Career intentions mainly evolved during clinical training, with smaller changes at the end of pre-clinical training. The proportion of students intending to practice primary care increased over time from 12.8% (year 3) to 24% (year 6). Caring for patients was the most highly rated motive for becoming a doctor. The importance of the motives cure diseases, saving lives, and vocation decreased over time. Primary care career intentions were positively associated with the motives altruism and private practice, and negatively associated with the motives prestige, academic interest and cure diseases. Conclusion Our study indicates that career intentions are not fixed and change mainly during clinical training, supporting the influence of clinical experiences on career-related choices. The impact of students’ motives on primary care career choice suggests strategies to increase the attractivity of this career, such as reinforcing students’ altruistic values and increasing the academic recognition of primary care.

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Background Medical students’ career intentions often change between matriculation and graduation, yet little is known about the precise timing and dynamics of individual students’ career decisions. This study expands on previous research by exploring the stability of individual students’ career intentions over four years and by analyzing associations between unstable career intentions and students’ characteristics. Methods Medical students from two classes were recruited into a cohort during their first academic year and completed a yearly survey over a four-year period (end of pre-clinical curriculum to graduation). Measures included career intention (specialty and practice type), personality, coping strategies, empathy, and motives for becoming a physician. The authors developed a score ranging from 0 to 10 quantifying instability of career intentions (0 = stable; 10 = unstable). The distribution of the score was analyzed descriptively, and the association between the score and other variables was quantified using a stepwise beta regression model. Results The sample included 262 students (61% females). The mean score was 3.07 with a median of 3. 18% of students (N = 46) did not change their specialty intention over the four years, whereas 10% (N = 26) changed every year. No further subgroups were identified between these extremes. An intention to work in private practice in year 3 and the motive care for patients were significantly associated with more stable career intentions. Conclusion Most students are situated on a continuum between the two extremes of being firmly committed and undecided. Extrinsic factors may be more important drivers of these fluctuations than personal characteristics and should be explored in future research. This study’s findings also provide avenues for supporting students in their career decision-making.