Related Experiment Video
Updated: Jun 11, 2025

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Predicting collaborative practice between midwives and obstetricians: A regression analysis
Liesa Beier1,2, Qendresa Thaqi3,4, Ans Luyben5,6
1Department of Obstetrics, University Hospital Zurich, Zurich, Switzerland.
Introduction:
Effective collaborative practice between midwives and obstetricians improves patient safety and obstetrical outcomes, but its implementation remains challenging. Therefore, its determinants need to be better understood. This study examined factors impacting collaborative practice (CP) between these professional groups.
Methods:
This study was a cross-sectional survey that took place in Swiss hospital labor wards in 2021. Collaborative practice perceptions of 70 midwives (57.4% response rate) and 44 obstetricians (29.0% response rate) were assessed using the Interprofessional Collaboration Scale, with the score serving as the main outcome. A total of 13 individual, behavioral, and organizational predictors were analyzed by multiple linear regression.
Results:
Participants rated collaborative practice with a median score of 3.1 (IQR: 2.8-3.4) out of a maximum score of 4.0. Results showed that five predictors significantly influenced collaborative practice: type of profession (β= -0.180; 95% CI: -0.296 - -0.040, p=0.011), trust/respect (β=0.343; 95% CI: 0.085-0.040, p=0.000), shared visions/goals (β=0.218; 95% CI: 0.030-0.204, p=0.009), workplace (β=0.253; 95% CI: 0.089-0.445, p=0.004) and shared power (β=0.163; 95% CI: 0.042-0.222, p=0.015). The model explained 66% of the variance (adjusted R2) in collaborative practice in labor wards.
Conclusions:
This study has identified key factors influencing CP in Swiss labor wards: workplace characteristics that require tailored CP models, and a power-sharing culture that fosters trust, respectful interactions and shared goals, requiring active exchange between midwives and obstetricians.
Related Concept Videos
Regression Toward the Mean
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Correlation and Regression

