Some worst-case datasets of deterministic first-order methods for solving binary logistic regression

Background: Gestational diabetes mellitus is a condition that affects many pregnancies and ethnicity appears to be a risk factor. Data indicate that approximately 18% of Tamil women are diagnosed with gestational diabetes mellitus. Today, approximately 50,000 of Tamils live in Switzerland. To date, there is no official tool available in Switzerland that considers the eating and physical activity habits of this migrant Tamil population living in Switzerland, while offering a quick overview of gestational diabetes mellitus and standard dietetics management procedures. The NutriGeD project led by Bern University of Applied Sciences in Switzerland aimed at closing this gap. The aim of this present study was to evaluate the implementation potential of the tools developed in the project NutriGeD for dietetic counseling before their wide scale launch in Swiss hospitals, clinics and private practices. Method: An online survey was developed and distributed to 50 recruited healthcare professionals working in the German speaking region of Switzerland from October – December 2016 (31% response rate). The transcultural tools were sent to participants together with the link to the online survey. The evaluation outcome was analysed using binary logistic regression and cross tabulation analysis with IBM SPSS version 24.0, 2016. Results: 94% (N=47) respondents believed that the transcultural tools had good potential for implementation in hospitals and private practices in Switzerland. A binary logistic regression analysis revealed that the age of participants had a good correlation (42.1%) on recommending the implementation potential of the transcultural tool. The participants with age group 34- 54 years old where the highest group to recommend the implementation potential of the transcultural tool and this was found to be statistically significant (p=0.05). 74% (34 out of 50) of the respondents clearly acknowledged the need for transcultural competence knowledge in healthcare practices. 80% (N =40) of the respondents agreed that the information presented in the counseling display folder was important and helpful while 60% (N= 30) agreed to the contents being clinically applicable. 90% (N=45) participants recommended the availability of the evaluated transcultural tools in healthcare settings in Switzerland. Conclusion: The availability in healthcare practice of the evaluated transcultural tools was greatly encouraged by the Swiss healthcare practitioners participating in the survey. While they confirmed the need for these transcultural tools, feed-backs for minor adjustments were given to finalize the tools before their official launch in practice. The developed materials will be made available for clinical visits, in both hospitals and private practices in Switzerland. The Migmapp© transcultural tool can serve as a good approach in assisting healthcare professionals in all fields, especially professionals who practice in areas associated with diet - related diseases or disorders associated with populations at risk.

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Intimate Partner Violence and its Association With Contraceptive Use Among Women in Pakistan

Pakistan Journal of Psychological Research ◽

10.33824/pjpr.2019.34.1.9 ◽

2019 ◽

Vol 34 (Spring 2019) ◽

pp. 157-173

Author(s):

Kashif Siddique ◽

Rubeena Zakar ◽

Ra’ana Malik ◽

Naveeda Farhat ◽

Farah Deeba

Keyword(s):

Intimate Partner Violence ◽

Logistic Regression ◽

Partner Violence ◽

Contraceptive Use ◽

Binary Logistic Regression ◽

Intimate Partner ◽

Married Women ◽

Reproductive Age ◽

Outcome Variable ◽

Odds Ratios

The aim of this study is to find the association between Intimate Partner Violence (IPV) and contraceptive use among married women in Pakistan. The analysis was conducted by using cross sectional secondary data from every married women of reproductive age 15-49 years who responded to domestic violence module (N = 3687) of the 2012-13 Pakistan Demographic and Health Survey. The association between contraceptive use (outcome variable) and IPV was measured by calculating unadjusted odds ratios and adjusted odds ratios with 95% confidence intervals using simple binary logistic regression and multivariable binary logistic regression. The result showed that out of 3687 women, majority of women 2126 (57.7%) were using contraceptive in their marital relationship. Among total, 1154 (31.3%) women experienced emotional IPV, 1045 (28.3%) women experienced physical IPV and 1402 (38%) women experienced both physical and emotional IPV together respectively. All types of IPV was significantly associated with contraceptive use and women who reported emotional IPV (AOR 1.44; 95% CI 1.23, 1.67), physical IPV (AOR 1.41; 95% CI 1.20, 1.65) and both emotional and physical IPV together (AOR 1.49; 95% CI 1.24, 1.72) were more likely to use contraceptives respectively. The study revealed that women who were living in violent relationship were more likely to use contraceptive in Pakistan. Still there is a need for women reproductive health services and government should take initiatives to promote family planning services, awareness and access to contraceptive method options for women to reduce unintended or mistimed pregnancies that occurred in violent relationships.

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Survey on turnover intention of scientific and technological workers based on the binary logistic regression model—a case study of XPCC

Information Management and Management Engineering ◽

10.2495/imme140591 ◽

2014 ◽

Author(s):

Zhui Liu ◽

Honglu Gou ◽

Lingying Kong

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Turnover Intention ◽

Logistic Regression Model ◽

Binary Logistic Regression ◽

Binary Logistic Regression Model

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Assessing Medical Evidence

10.1093/oso/9780198747048.003.0007 ◽

2018 ◽

Author(s):

Jacob Stegenga

Keyword(s):

Quality Assessment ◽

Randomized Trials ◽

Empirical Studies ◽

Allocation Concealment ◽

Assessment Tools ◽

Medical Evidence ◽

Empirical Methods ◽

First Order ◽

First Order Methods

Medical scientists employ ‘quality assessment tools’ to assess evidence from medical research, especially from randomized trials. These tools are designed to take into account methodological details of studies, including randomization, subject allocation concealment, and other features of studies deemed relevant to minimizing bias. There are dozens of such tools available. They differ widely from each other, and empirical studies show that they have low inter-rater reliability and low inter-tool reliability. This is an instance of a more general problem called here the underdetermination of evidential significance. Disagreements about the quality of evidence can be due to different—but in principle equally good—weightings of the methodological features that constitute quality assessment tools. Thus, the malleability of empirical research in medicine is deep: in addition to the malleability of first-order empirical methods, such as randomized trials, there is malleability in the tools used to evaluate first-order methods.

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Least Likely Observations in Regression Models for Categorical Outcomes

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x0200200306 ◽

2002 ◽

Vol 2 (3) ◽

pp. 296-300 ◽

Cited By ~ 1

Author(s):

Jeremy Freese

Keyword(s):

Logistic Regression ◽

Regression Models ◽

Binary Logistic Regression ◽

Positive Outcome ◽

Ordinary Least Squares ◽

Parameter Estimates ◽

Least Squares Regression ◽

Binary Logistic Regression Model ◽

Ordered Logistic Regression ◽

Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

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