BackgroundThe maternal 25-hydroxy vitamin D (25OHD) insufficiency is related to adverse maternal and neonatal outcome. The 25OHD content of breast milk is dependent on 25OHD status of the mothers. We undertook this study to ascertain the 25OHD status and its determinants in the nursing mothers of the south Punjab, Pakistan.MethodsWe recruited 67 mothers for this cross-sectional study by convenience sampling from August 2010 to June 2011 to ascertain their serum 25OHD level & its determinants. We used SPSS 23.0 for analyses.ResultsThe mean age of the mothers was 25.75 ± 4.4 years. The median age (and mode) was 25 years (range 18-37 years). The majority of mothers were less than 25 years of age (62.7%), uneducated (68.7%), from rural area (70.1%), lived in open houses with ample sun exposure (85.1%) and belonged to low socioeconomic strata (71.6%).Serum 25OHD ranged from 7.2 to 43.8 nmol/L with a mean of 20.87 ± 7.69 nmol/L. The median and mode were 21.8 nmol/L & 24.0 nmol/L, respectively. The proportion of mothers with 25OHD < 20 nmol/L (severe deficiency) was 44.8%, < 30 nmol/L (deficiency) 49.3% and < 50 nmol/L (insufficiency) 5.9%. All had 25OHD below 50 nmol/L. The oral supplementation with vitamin D (vD) was the only significant determinant of vitamin D sufficiency.ConclusionsThe majority of Pakistani mothers in south Punjab are vD deficient & universal vD supplementation is the need of the hour to improve health outcomes in mothers & infants.
PurposeThis study aims to investigate the cybersecurity awareness manifested as protective behavior to explain self-disclosure in social networking sites. The disclosure of information about oneself is associated with benefits as well as privacy risks. The individuals self-disclose to gain social capital and display protective behaviors to evade privacy risks by careful cost-benefit calculation of disclosing information.Design/methodology/approachThis study explores the role of cyber protection behavior in predicting self-disclosure along with demographics (age and gender) and digital divide (frequency of Internet access) variables by conducting a face-to-face survey. Data were collected from 284 participants. The model is validated by using multiple hierarchal regression along with the artificial intelligence approach.FindingsThe results revealed that cyber protection behavior significantly explains the variance in self-disclosure behavior. The complementary use of five machine learning (ML) algorithms further validated the model. The ML algorithms predicted self-disclosure with an area under the curve of 0.74 and an F1 measure of 0.70.Practical implicationsThe findings suggest that costs associated with self-disclosure can be mitigated by educating the individuals to heighten their cybersecurity awareness through cybersecurity training programs.Originality/valueThis study uses a hybrid approach to assess the influence of cyber protection behavior on self-disclosure using expectant valence theory (EVT).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.