Considering the significant inverse relationship between the score of patient quality care and the dimension of moral conflict experience, it seems when nurses make moral decisions, they experience a conflict between personal and professional values in their careers and thus experience moral tension. If this tension is not resolved properly, it can provide a way for them to distance themselves from patients, thereby making nurses indifferent to moral care.

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‫ﻧﺸﺮﻳﻪ‬ ‫دوره‬ ‫اﻟﺒﺮز،‬ ‫ﭘﺰﺷﻜﻲ‬ ‫ﻋﻠﻮم‬ ‫داﻧﺸﮕﺎه‬ ‫آﻣﺎري‬ ‫آﻧﺎﻟﻴﺰ‬ ‫آﻣﺎري‬ ‫اﻓﺰار‬ ‫ﻧﺮم‬ ‫از‬

Background:Preterm labor is a major cause of perinatal morbidity and mortality and it might be predicted by assessing the cervical change.Objective: To assess the association between absence of cervical gland area (CGA) and spontaneous preterm labor (SPTL).Materials and Methods: This prospective cohort study was performed on 200 singleton pregnant women with a history of SPTL, second-trimester abortion in the previous pregnancy or lower abdominal pain in current pregnancy. Each patient underwent one transvaginal ultrasound examination between 14-28 wk of gestation. Cervical length was measured and CGA was identified and their relationship with SPTL before 35 and 37 wk gestation was evaluated using STATA software version 10.Results:The mean of cervical length was 36.5 mm (SD=8.4), the shortest measurement was 9 mm, and the longest one was 61 mm. Short cervical length (≤18mm) was significantly associated with SPTL before 35 and 37 wk gestation.Cervical gland area (the hypoechogenic or echogenic area around the cervical canal) was present in 189 (94.5%) patients. Absent of CGA had a significant relationship with SPTL before 35 and 37 wk gestation (p=0.01 and p<0.001, respectively). Cervical length was shorter in women with absent CGA in comparison with subjects with present CGA: 37±10 mm in CGA present group and 23±9 mm in CGA absent group (p<0.001).Conclusion:Our study showed that cervical gland area might be an important predictor of SPTL which should be confirmed with further researches.

BackgroundFunctional limitation is one of the most important health - related concerns of diabetic patients. This study aimed to identify the factors associated with functional limitation among diabetic patients using generalized additive model (GAM) as a flexible technique to reveal the non - linear and non - monotonic association between the response and a set of independent variables.MethodsThe source data belonged to two cross - sectional studies conducted in 2014. A total of 694 people with type 2 diabetes in the age range of 31 - 70 years were selected via convenience sampling from diabetes clinics in Ardabil and Tabriz. The data were collected by interviewers using structured questionnaires and checklists. The functional capacity was measured using the physical functioning subscale of the Medical Outcomes Study Short Form 36 - Item Health Survey (SF36). Participants with a total functional capacity of less than 90 were considered to have “moderate or high level of functional limitation.” To identify the factors associated with functional limitation and reveal the shape of associations, the GAM procedure with “logit” link function was applied to the dataset of 378 diabetic patients without any missing data by smoothening of the effect of underlying factors. The Akaike information criterion (AIC) as the relative quality of the model’s criterion was computed for GAM and compared with AIC of the simple logistic regression.ResultsSex (P = 0.029), age (P < 0.001), BMI (P = 0.029), and SBP (P = 0.04) were significant in the GAM. Moreover, age with a linear function (df = 0.98), BMI with quadratic function (df = 1.75), and SBP with the degree 1.33 were significantly related to functional capacity. AIC of the GAM was lower than that of the logistic model.ConclusionsIn our sample, GAM could identify some linear and nonlinear associations between underlying factors and functional limitation in diabetic patients. These complex associations could relatively increase the fit quality of the GAM when compared to logistic regression.