Laura Hill, Louise Parker, Jenifer McGuire and Rayna Sage
Over the past 30 years, researchers have documented effective, theory‐based programmes and practices that improve the health and well‐being of children. In order to produce…
Abstract
Over the past 30 years, researchers have documented effective, theory‐based programmes and practices that improve the health and well‐being of children. In order to produce measurable improvements in public health, such practices must be institutionalised; however, there are a number of barriers to translating what we know from science to what we do in practice. In the present article, we discuss a number of those barriers, including: cultural differences between those who espouse a public health, prevention science approach versus those who espouse a strengths‐building, health promotion approach; practical difficulties in documenting the evidence base for existing or newly developed programmes and practices; and inflexibility of standardised programmes and resulting insensitivity to local contexts. We discuss common ground between prevention and promotion perspectives and highlight emerging methods that facilitate the adoption of science‐based practice into community‐based services.
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Jodi Dworkin, Ellie McCann and Jenifer K. McGuire
The current study was designed to examine how and why divorced parents use computers and the Internet for communication with their coparent and with their child(ren).
Abstract
Purpose
The current study was designed to examine how and why divorced parents use computers and the Internet for communication with their coparent and with their child(ren).
Methodology/approach
The current study utilized the uses and gratification perspective. A subsample of 178 divorced parents with at least one child aged 25 or younger from a larger research project participated. Parents were recruited to participate in a 15-minute online survey through email listservs with a nationwide and demographically diverse reach.
Findings
Analyses revealed that divorced parents are active users of technology, for communicating with their child(ren) as well as with the child(ren)’s other parent. In addition, parents were comfortable using the Internet and accessing online parenting information, citing few barriers to use.
Research limitations/implications
We did not capture the reasons for communicating or the content of communication. Future research should use innovative methodologies and measures to better understand the use of specific technologies and tools to negotiate boundaries between coparents living apart. In addition, a larger, more diverse sample might reveal different patterns of divorced parents’ technology use.
Practical implications
Technology allows for asynchronous communication, staying up to date, making plans, and making decisions with minimal interaction, and thus maintaining boundaries. Our evidence suggests technology could help parents find areas of agreement around their children’s lives in a less contentious environment.
Originality/value
This study provides the essential groundwork for further examination of ways to support coparent communication via technology.
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Jenifer Sunrise Winter and Elizabeth Davidson
This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning…
Abstract
Purpose
This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions.
Design/methodology/approach
This conceptual paper highlights the scale and scope of PHI data consumed by deep learning algorithms and their opacity as novel challenges to health data governance.
Findings
This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.
Social implications
Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security.
Originality/value
This is the first paper focusing on health data governance in relation to AI/machine learning.