Introducing a new measure for assessing self-efficacy in response to air pollution hazards for pregnant women

A self-efficacy instrument should be condition-specific. There are several instruments for measuring self-efficacy, but none are air pollution-specific. This study aimed to develop a self-efficacy measure for assessing pregnant women's responses to air pollution hazards. A random sample of pregnant women aged between 18 and 35 years attending three prenatal care centers were entered into the study. Prenatal care centers randomly selected from a list of centers located in different geographical regions of Tehran, Iran. After careful consideration and performing content and face validity, a 4-item measure was developed and participants completed the questionnaire. Reliability was estimated using internal consistency and validity was assessed by performing confirmatory factor analysis (CFA) and known group comparison. In all 200 eligible pregnant women were studied. The mean age of participants was 26.9 (SD = 4.8) years and it was 27.9 (SD = 9.1) weeks for gestational age. The findings showed almost perfect results for both content validity ratio (CVR = 1) and content validity index (CVI = 1). The confirmatory factor analysis indicated a good fit to the data, and known group comparison revealed satisfying results. Internal consistency as measured by the Cronbach's alpha coefficient was found to be 0.74. In general, the findings suggest that this new generated scale is a reliable and valid specific measure of self-efficacy in response to air pollution hazards for pregnant women. However, further studies are needed to establish stronger psychometric properties for the questionnaire. © 2013 Araban et al.; licensee BioMed Central Ltd

Recommender Systems: Sources of Knowledge and Evaluation Metrics

Recommender or Recommendation Systems (RS) aim to help users dealing with information overload: finding relevant items in a vast space of resources. Research on RS has been active since the development of the first recommender sys-tem in the early 1990s, Tapestry, and some articles and books that survey algorithms and application domains have been published recently. However, these surveys have not extensively covered the different types of information used in RS (sources of knowledge), and only a few of them have reviewed the different ways to assess the quality and performance of RS. In order to bridge this gap, in this chapter we present a classification of recommender systems, and then we focus on presenting the main sources of knowledge and evaluation metrics that have been described in the research literature