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.

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.

The global navigation satellite system (GNSS) daily position time series are often described as the sum of stochastic processes and geophysical signals which allow to study global and local geodynamical effects such as plate tectonics, earthquakes, or ground water variations. In this work, we propose to extend the Generalized Method of Wavelet Moments (GMWM) to estimate the parameters of linear models with correlated residuals. This statistical inferential framework is applied to GNSS daily position time-series data to jointly estimate functional (geophysical) as well as stochastic noise models. Our method is called GMWMX, with X standing for eXogenous variables: it is semi-parametric, computationally efficient and scalable. Unlike standard methods such as the widely used maximum likelihood estimator (MLE), our methodology offers statistical guarantees, such as consistency and asymptotic normality, without relying on strong parametric assumptions. At the Gaussian model, our results (theoretical and obtained in simulations) show that the estimated parameters are similar to the ones obtained with the MLE. The computational performances of our approach have important practical implications. Indeed, the estimation of the parameters of large networks of thousands of GNSS stations (some of them being recorded over several decades) quickly becomes computationally prohibitive. Compared to standard likelihood-based methods, the GMWMX has a considerably reduced algorithmic complexity of order $$\mathcal {O}\{\log (n) n\}$$ O { log ( n ) n } for a time series of length n. Thus, the GMWMX appears to provide a reduction in processing time of a factor of 10–1000 compared to likelihood-based methods depending on the considered stochastic model, the length of the time series and the amount of missing data. As a consequence, the proposed method allows the estimation of large-scale problems within minutes on a standard computer. We validate the performances of our method via Monte Carlo simulations by generating GNSS daily position time series with missing observations and we consider composite stochastic noise models including processes presenting long-range dependence such as power law or Matérn processes. The advantages of our method are also illustrated using real time series from GNSS stations located in the Eastern part of the USA.

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Background We propose a new approach for designing personalized treatment for colorectal cancer (CRC) patients, by combining ex vivo organoid efficacy testing with mathematical modeling of the results. Methods The validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) was used to identify four low-dose synergistic optimized drug combinations (ODC) in 3D human CRC models of cells that are either sensitive or resistant to first-line CRC chemotherapy (FOLFOXIRI). Our findings were obtained using second order linear regression and adaptive lasso. Results The activity of all ODCs was validated on patient-derived organoids (PDO) from cases with either primary or metastatic CRC. The CRC material was molecularly characterized using whole-exome sequencing and RNAseq. In PDO from patients with liver metastases (stage IV) identified as CMS4/CRIS-A, our ODCs consisting of regorafenib [1 mM], vemurafenib [11 mM], palbociclib [1 mM] and lapatinib [0.5 mM] inhibited cell viability up to 88%, which significantly outperforms FOLFOXIRI administered at clinical doses. Furthermore, we identified patient-specific TGMO-based ODCs that outperform the efficacy of the current chemotherapy standard of care, FOLFOXIRI. Conclusions Our approach allows the optimization of patient-tailored synergistic multi-drug combinations within a clinically relevant timeframe. Graphical Abstract