Gender-Based Violence and Pregnancy Outcomes among Couples and Cohabiting Partners in Nigeria

Over the last two decades, the international community has expressed concerns on the increasing occurrence of gender-based violence and related consequences, particularly in African and many low-and middle-income countries. The 2006 United Nations General Assembly declaration on elimination of violence against women has been applauded across many settings. Intimate partner violence is a typical example of gender-based violence that occurs among males and females who have intimate relationships either as husbands and wives, or are in cohabiting relationship. This paper examines factors likely to influence gender-based violence and effects on pregnancy outcomes in Nigeria. The study used the 2013 Nigeria Demographic and Health Survey (NDHS) couple data set of 8658 couples aged 15-49 for women and 15-59 for men. 6,961 were interviewed for domestic violence. Analysis employed univariate, bivariate and multivariate techniques i.e. binary logistic regression. Pregnancy outcome, measured as either live birth or stillbirth is the ultimate dependent variable. Explanatory factors are of two types (1) intervening factors are, physical, and emotional violence, and (2) background factors including age, residence, education, employment status, and religion among others. The results showed that residence, employment status, religion and partner’s age were significant factors explaining emotional, and physical violence among couples (p<0.05) While physical violence and emotional violence were significant associated with pregnancy outcome of either a live birth or still birth (P<0.05). The study also showed that respondents whose husband takes alcohol have a higher odds of experiencing violence while those who are working and educated beyond primary school also have higher odds of experiencing violence. These findings have significant implications for policy and programmes geared to improve on gender equity, and reproductive health of women in Nigeria

A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects

Funder: The Health FoundationFunder: National Institute for Health ResearchAbstract: Background: The design and execution of measurement in quality improvement (QI) initiatives is often poor. Better guidance on “what good looks like” might help to mitigate some of the problems. We report a consensus-building process that sought to identify which features are important to include in QI measurement plans. Methods: We conducted a three-stage consensus-building approach: (1) identifying the list of features of measurement plans that were potential candidates for inclusion based on literature review and the study team’s experience; (2) a two-round modified Delphi exercise with a panel of experts to establish consensus on the importance of these features; and (3) a small in-person consensus group meeting to finalise the list of features. Results: A list of 104 candidate questions was generated. A panel of 19 experts in the Delphi reviewed these questions and produced consensus on retaining 46 questions in the first round and on a further 22 in the second round. Thematic analysis of open text responses from the panellists suggested a number of areas of debate that were explicitly considered by the consensus group. The exercise yielded 74 questions (71% of 104) on which there was consensus in five categories of measurement relating to: design, data collection and management, analysis, action, and embedding. Conclusions: This study offers a consensus-based view on the features of a good measurement plan for a QI project in healthcare. The results may be of use to QI teams, funders and evaluators, but are likely to require further development and testing to ensure feasibility and usefulness

A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects

Funder: The Health FoundationFunder: National Institute for Health ResearchAbstract: Background: The design and execution of measurement in quality improvement (QI) initiatives is often poor. Better guidance on “what good looks like” might help to mitigate some of the problems. We report a consensus-building process that sought to identify which features are important to include in QI measurement plans. Methods: We conducted a three-stage consensus-building approach: (1) identifying the list of features of measurement plans that were potential candidates for inclusion based on literature review and the study team’s experience; (2) a two-round modified Delphi exercise with a panel of experts to establish consensus on the importance of these features; and (3) a small in-person consensus group meeting to finalise the list of features. Results: A list of 104 candidate questions was generated. A panel of 19 experts in the Delphi reviewed these questions and produced consensus on retaining 46 questions in the first round and on a further 22 in the second round. Thematic analysis of open text responses from the panellists suggested a number of areas of debate that were explicitly considered by the consensus group. The exercise yielded 74 questions (71% of 104) on which there was consensus in five categories of measurement relating to: design, data collection and management, analysis, action, and embedding. Conclusions: This study offers a consensus-based view on the features of a good measurement plan for a QI project in healthcare. The results may be of use to QI teams, funders and evaluators, but are likely to require further development and testing to ensure feasibility and usefulness

A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects.

BACKGROUND:The design and execution of measurement in quality improvement (QI) initiatives is often poor. Better guidance on "what good looks like" might help to mitigate some of the problems. We report a consensus-building process that sought to identify which features are important to include in QI measurement plans. METHODS:We conducted a three-stage consensus-building approach: (1) identifying the list of features of measurement plans that were potential candidates for inclusion based on literature review and the study team's experience; (2) a two-round modified Delphi exercise with a panel of experts to establish consensus on the importance of these features; and (3) a small in-person consensus group meeting to finalise the list of features. RESULTS:A list of 104 candidate questions was generated. A panel of 19 experts in the Delphi reviewed these questions and produced consensus on retaining 46 questions in the first round and on a further 22 in the second round. Thematic analysis of open text responses from the panellists suggested a number of areas of debate that were explicitly considered by the consensus group. The exercise yielded 74 questions (71% of 104) on which there was consensus in five categories of measurement relating to: design, data collection and management, analysis, action, and embedding. CONCLUSIONS:This study offers a consensus-based view on the features of a good measurement plan for a QI project in healthcare. The results may be of use to QI teams, funders and evaluators, but are likely to require further development and testing to ensure feasibility and usefulness

A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects

Funder: The Health FoundationFunder: National Institute for Health ResearchAbstract: Background: The design and execution of measurement in quality improvement (QI) initiatives is often poor. Better guidance on “what good looks like” might help to mitigate some of the problems. We report a consensus-building process that sought to identify which features are important to include in QI measurement plans. Methods: We conducted a three-stage consensus-building approach: (1) identifying the list of features of measurement plans that were potential candidates for inclusion based on literature review and the study team’s experience; (2) a two-round modified Delphi exercise with a panel of experts to establish consensus on the importance of these features; and (3) a small in-person consensus group meeting to finalise the list of features. Results: A list of 104 candidate questions was generated. A panel of 19 experts in the Delphi reviewed these questions and produced consensus on retaining 46 questions in the first round and on a further 22 in the second round. Thematic analysis of open text responses from the panellists suggested a number of areas of debate that were explicitly considered by the consensus group. The exercise yielded 74 questions (71% of 104) on which there was consensus in five categories of measurement relating to: design, data collection and management, analysis, action, and embedding. Conclusions: This study offers a consensus-based view on the features of a good measurement plan for a QI project in healthcare. The results may be of use to QI teams, funders and evaluators, but are likely to require further development and testing to ensure feasibility and usefulness