Publications by authors named "R Granovsky"

Background: Restrictive gender norms exacerbate health inequalities all over the world. More specifically, they prevent women from seeking preventive health services, constrain women's economic empowerment, and are associated with reproductive health decision making. Gender norms, a subset of social norms, are dynamic and change over time.

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Article Synopsis
  • The study highlights the importance of documenting social determinants of health (SDoH) through Z codes in clinical care, noting that they are rarely used in emergency department (ED) charts, with only 1-2% of inpatient cases including them.
  • An analysis of ED data from 2016-2019 found that the use of Z codes increased slightly, from 0.65% to 1.17%, with certain demographics (specifically younger adults, males, Black patients, and those on Medicaid or self-pay) showing higher likelihoods of Z code usage.
  • The findings suggest that while Z code documentation in EDs remains low, it is improving, indicating a need for
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Much of the methodological literature on rapid qualitative analysis describes processes used by a relatively small number of researchers focusing on one study site and using rapid analysis to replace a traditional analytical approach. In this paper, we describe the experiences of a transnational research consortium integrating both rapid and traditional qualitative analysis approaches to develop social theory while also informing program design. Research was conducted by the Innovations for Choice and Autonomy (ICAN) consortium, which seeks to understand how self-injection of the contraceptive subcutaneous depot medroxyprogesterone acetate (DMPA-SC) can be implemented in a way that best meets women's needs, as defined by women themselves.

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Research calls for the sexual and reproductive rights field to prioritize gender norms to ensure that women can act on their reproductive rights. However, there is a gap in accepted measures. We addressed this by including important theoretical components of gender norms: differentiating between descriptive and injunctive norms and adding a referent group.

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Article Synopsis
  • The study aims to identify patients who are unlikely to pay their emergency department bills, improving the experience for both patients and healthcare providers.
  • Three machine learning methods (logistic regression, decision tree, and random forest) were used to analyze over a million patients' data to predict payment likelihood within 90 days.
  • The decision tree model proved effective, accurately predicting 87% of cases where patients would not pay, highlighting opportunities to better assist those needing financial help.
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