Integrated Administrative Data for Early Childhood Iowa: A Governance Model to inform Policy and Program Collaboration

In response to demands on public systems to do more, do better, and cost less, the value of integrated administrative data systems (IDS) for social policy is increasing (Fantuzzo & Culhane, 2016). This is particularly relevant in programming for young children where services are historically fragmented, disconnected from systems serving school-aged children, and siloed among health, human services, and education agencies. Guided by the vision that Iowa’s early childhood system will be effectively and efficiently coordinated to support healthy families, we are developing an early childhood IDS to address this disconnection and facilitate relevant and actionable social policy research. Iowa’s IDS is a state-university partnership that acknowledges the need for agencies to retain control of their data while enabling it to be integrated across systems for social policy research. The innovative governance model deliberately incorporates procedures for stakeholder engagement at critical tension points between executive leaders, program managers, researchers, and practitioners. Standing committees (Governance Board, Data Stewardship, and Core team) authorize and implement the work of the IDS, while ad-hoc committees are solicited for specific projects to advise and translate research into practice. This paper will articulate the Iowa IDS governance model that was informed by means tested principles articulated by the Actionable Intelligence for Social Policy Network. It will include our collaborative development process; articulated mission and principles that guided discussions about legal authorization, governance, and use cases; and the establishment of governance committees to implement our vision for ethical and efficient use of administrative data for social policy

Infertility and fertility intentions, desires, and outcomes among US women

BACKGROUND AND OBJECTIVE Little is known about how the experience of infertility or identification as someone with infertility shapes women’s fertility intentions, desires, or birth outcomes. The purpose of this paper is to help fill this gap in knowledge for fertility-intentions research. METHODS Using data from the National Survey of Fertility Barriers (NSFB), we use linear and logistic regression methods to assess how infertility and parity statuses are associated with fertility intentions and desires, as well as how statuses at one point in time predict birth three years later. RESULTS We find that infertility is associated with lower fertility intentions. Women who have experienced infertility and/or identify as a person with infertility, however, express greater desires to have a baby and a higher ideal number of children. Women who meet the medical criteria for infertility are less likely than fecund women to give birth, despite greater desires

Infertility and fertility intentions, desires, and outcomes among US women

BACKGROUND AND OBJECTIVE Little is known about how the experience of infertility or identification as someone with infertility shapes women’s fertility intentions, desires, or birth outcomes. The purpose of this paper is to help fill this gap in knowledge for fertility-intentions research. METHODS Using data from the National Survey of Fertility Barriers (NSFB), we use linear and logistic regression methods to assess how infertility and parity statuses are associated with fertility intentions and desires, as well as how statuses at one point in time predict birth three years later. RESULTS We find that infertility is associated with lower fertility intentions. Women who have experienced infertility and/or identify as a person with infertility, however, express greater desires to have a baby and a higher ideal number of children. Women who meet the medical criteria for infertility are less likely than fecund women to give birth, despite greater desires

Infertility and fertility intentions, desires, and outcomes among US women

Objective: Little is known about how the experience of infertility or identification as someone with infertility shapes women's fertility intentions, desires, or birth outcomes. The purpose of this paper is to help fill this gap in knowledge for fertility-intentions research. Methods: Using data from the National Survey of Fertility Barriers (NSFB), we use linear and logistic regression methods to assess how infertility and parity statuses are associated with fertility intentions and desires, as well as how statuses at one point in time predict birth three years later. Results: We find that infertility is associated with lower fertility intentions. Women who have experienced infertility and/or identify as a person with infertility, however, express greater desires to have a baby and a higher ideal number of children. Women who meet the medical criteria for infertility are less likely than fecund women to give birth, despite greater desires. Conclusions: These findings have important theoretical implications for our understanding of the meaning of fertility intentions for those who think their ability to achieve their intentions is uncertain, as well as for empirical research on fertility

The readiness assurance process in online team‐based learning classrooms

This chapter describes best practices to address the challenges posed for implementing the second stage of team-based learning (TBL), the Readiness Assurance Process (RAP), in online settings, and uses the experience of expert TBL users to suggest strategies for maintaining the essential aspects of the pedagogy.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Ten simple rules to ruin a collaborative environment

Trigger warning: Here, you will find a bit of satire, written from the not-so-funny, real experiences of the authors who have been involved in “team science” collaboratives. The material presented below covers topics that readers may find offensive or even traumatizing. We present a breakdown (pun intended) of how to ruin a functioning collaboration, rather than how to build one. The ideas contained in this work were developed during two virtual meetings of members of the Agricultural Genome to Phenome Initiative (AG2PI; www.ag2pi.org) community and leadership team in May and June of 2021. In these sessions, we looked back at collaborative projects that were miserable failures and recalled what went wrong so we could avoid making the same mistakes in the future. We also considered what signals we might have missed that could have saved us some misery and where we might have had some blind spots (but should have seen coming). As a side note, having worked on dysfunctional teams from time to time, we found writing this set of rules to be both cathartic and vastly cheaper than therapy. If you are not prepared for what will likely be the occasional, “Yikes, that sounds terribly familiar!” or would rather read some more upbeat advice, here are a few options we recommend: Vicens and Bourne [1], de Grijs [2], Knapp and colleagues [3], Cechova [4], Sahneh and colleagues [5], and Gewin [6].This article is published as Lawrence-Dill CJ, Allscheid RL, Boaitey A, Bauman T, Buckler ES IV, Clarke JL, et al. (2022) Ten simple rules to ruin a collaborative environment. PLoS Comput Biol 18(4): e1009957. https://doi.org/10.1371/journal.pcbi.1009957. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted