The purpose of the study was to develop and test a self-report instrument that describes the frequency that intrapartum nurses perform labor support and physiologic interventions and the nurses' beliefs of the helpfulness of their interventions. The Labor Support and Physiologic Intervention Scale (LSPIS) uses a Likert-style format in a 5-category range of answer options to collect data on nurses' practices to enhance the progress and process of women's labors. Social support theory and physiologic theory provided a conceptual foundation, while the scientific literature on labor support and intrapartum care provided a basis for the original 57 items used in the pilot (n=10) and the Phase I study (n=307). Five labor support experts evaluated the items, with an interrater agreement of .95 and a content validity index of .93. The 57-item LSPIS had an internal reliability of .93 and .95 for the frequency and helpfulness portions (respectively). Exploratory factor analysis identified a 6-factor solution accounting for 48% of the variance that matched the social support and labor support dimensions. The LSPIS was revised and shortened to 26 items based upon the Phase I data analyses. The Phase II study had 472 respondents recruited from two professional listserves. The internal consistency reliability of the revised LSPIS was .90 and .91 for the frequency and helpfulness portions (respectively). Construct validity was evaluated by exploratory factor analysis, which found several acceptable factor solutions that were consistent with the theoretical framework. A six-factor solution accounted for 59% of the variance and had the following components: instrumental or physical support, emotional support, partner support & information/advice, advocacy, mother-directed pushing, and sustenance. Known groups technique and content analysis of nurses' responses to open-ended questions further evaluated construct validity, while discriminant validity was ascertained using a short version of the Marlowe-Crowne Social Desirability Scale. Further data analyses explored relationships among LSPIS scores and various organizational, geographic, and provider variables. In both phases, instrument psychometrics were acceptable and the scale demonstrated acceptable content and construct validity. Further revisions and future uses of the instrument can be guided by the recommendations provided. / text

For most of the 20th century, the saying “once a cesarean, always a cesarean” was a rule in the United States. Today, the National Institutes of Health (NIH) opposes the dictum and urges women to consider trial of labor after cesarean (TOLAC). However, the factors that lead to a successful outcome remain unclear, as research continues to be conducted in hopes of creating a predictive model for vaginal birth after cesarean (VBAC) success. The NIH’s request for more research in this area of obstetrics led to this retrospective cohort study of all TOLACs at Marin General Hospital (MGH) from 2000-2013. All labor trials were studied for patient demographics, details of labor, maternal and neonatal morbidities, insurance, and provider type. After confirming the quality of the data, verifying inclusion criteria and ignoring cases with missing data, a data set of 745 TOLACs with 13 explanatory variables of interest was prepared. A forward stepwise (Likelihood Ratio) binary logistic regression was run in IBM® SPSS® Statistics in order to create a model that could determine which variables were most predictive of delivery outcome in TOLAC patients. Ultimately, seven variables were predictive and were included in the model. Of the seven, the most predictive variable in determining VBAC success was provider type. The model concluded that a woman’s odds of having a successful VBAC were almost four times greater if she began her delivery with a certified nurse midwife, than if she began her deliver with a physician (odds ratio 0.27, 95% CI 0.17-0.44; < 0.01). The results from this study mimic the results of other models, and introduce labor support as a key factor in predicting VBAC success.