Need for recovery from work: evaluating short-term effects of working hours,patterns and schedules

In this paper working hours, patterns and work schedules of employees were evaluated in terms of need for recovery from work. Self-administered questionnaire data from employees of the Maastricht Cohort Study on Fatigue at Work (n = 12,095) were used. Poisson regression analyses and multivariate logistic regression analyses revealed that higher working hours a day and working hours a week generally went together with more need for recovery from work. Overtime work was particularly associated with higher need for recovery from work in both genders. Both male and female three-shift or irregular shift workers had higher odds of elevated need for recovery compared to day workers. When additionally controlling for work-related factors, need for recovery levels among shift workers substantially lowered. This study clearly showed that working hours and schedules are associated with need for recovery from work, with different associations for men and women. Especially the associations between work schedules and need for recovery from work were very interrelated with other work-related factors. Future studies could further investigate the possibility that shift work might function as a proxy of other work-related factors that explain the different levels in need for recovery from work, or that job demands are perceived higher among shift workers and may therefore lead to more need for recovery from work.     

A new method for mining Frequent Weighted Itemsets based on WIT-trees

The mining frequent itemsets plays an important role in the mining of association rules. Frequent itemsets are typically mined from binary databases where each item in a transaction may have a different significance. Mining Frequent Weighted Itemsets (FWI) from weighted items transaction databases addresses this issue. This paper therefore proposes algorithms for the fast mining of FWI from weighted item transaction databases. Firstly, an algorithm for directly mining FWI using WIT-trees is presented. After that, some theorems are developed concerning the fast mining of FWI. Based on these theorems, an advanced algorithm for mining FWI is proposed. Finally, a Diffset strategy for the efficient computation of the weighted support for itemsets is described, and an algorithm for mining FWI using Diffsets presented. A complete evaluation of the proposed algorithms is also presented.     

Validation of caregiver interviews to diagnose common causes of severe neonatal illness

The objective of this study was to validate retrospective caregiver interviews for diagnosing major causes of severe neonatal illness and death. A convenience sample of 149 infants aged < 28 days with one or more suspected diagnoses of interest (low birthweight/severe malnutrition, preterm birth, birth asphyxia, birth trauma, neonatal tetanus, pneumonia, meningitis, septicaemia, diarrhoea, congenital malformation or injury) was taken from patients admitted to two hospitals in Dhaka, Bangladesh. Study paediatricians performed a standardised history and physical examination and ordered laboratory and radiographic tests according to study criteria. With a median interval of 64.5 days after death or hospital discharge, caregivers of 118 (79%) infants were interviewed about their child's illness. Using reference diagnoses based on predefined clinical and laboratory criteria, the sensitivity and specificity of particular combinations of signs (algorithms) reported by the caregivers were ascertained. Sufficient numbers of children with five reference standard diagnoses were studied to validate caregiver reports. Algorithms with sensitivity and specificity > 80% were identified for neonatal tetanus, low birthweight/severe malnutrition and preterm delivery. Algorithms with specificities > 80% for birth asphyxia and pneumonia had sensitivities < 70%, or alternatively had high sensitivity with lower specificity. In settings with limited access to medical care, retrospective caregiver interviews provide a valid means of diagnosing several of the most common causes of severe neonatal illness and death.