The Importance of Time Congruity in the Organisation.

In 1991 Kaufman, Lane, and Lindquist proposed that time congruity in terms of an individual's time preferences and the time use methods of an organisation would lead to satisfactory performance and enhancement of quality of work and general life. The research reported here presents a study which uses commensurate person and job measures of time personality in an organisational setting to assess the effects of time congruity on one aspect of work life, job-related affective well-being. Results show that time personality and time congruity were found to have direct effects on well-being and the influence of time congruity was found to be mediated through time personality, thus contributing to the person–job (P–J) fit literature which suggests that direct effects are often more important than indirect effects. The study also provides some practical examples of ways to address some of the previously cited methodological issues in P–J fit research

Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers

Background Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data. Methods We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Results Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh). Conclusions Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data