Abstract:Background
Case‐finding for dementia is practised by general practitioners (GPs) in Australia but without an awareness of community preferences. We explored the values and preferences of informed community members around case‐finding for dementia in Australian general practice.
Design, setting and participants
A before and after, mixed‐methods study in Gold Coast, Australia, with ten community members aged 50‐70.
Intervention
A 2‐day citizen/community jury. Participants were informed by experts about dementia,… Show more
“…Applying the framework : The community jurors offered a wide range of reasons for their recommendation against case‐finding for dementia. These included the following: the absence of effective treatments, timing in the course of disease, impact of the results on the individual's mental and emotional health, and others . For example:Juror 10: “I see that to be diagnosed and told that you are destined to become a person with dementia, will be devastating for anyone.…”
Section: Resultsmentioning
confidence: 99%
“…Using the coding framework developed from stage 1, we piloted the application of the framework on transcripts of the jurors’ private deliberations during a recent community jury on case‐finding for dementia that was conducted in March 2017. We asked participants of this CJ “Should the health system encourage General Practitioners to practice ‘case‐finding’ of dementia in people older than 50?”.…”
Section: Methodsmentioning
confidence: 99%
“…The Jury consisted of 10 participants (five male and five female), whose average age was 62, and educational level was mixed, ranging from some secondary education to university postgraduate. Full details of that CJ have been reported elsewhere …”
Background
Public participation in health policy decision making is thought to improve the quality of the decisions and enhance their legitimacy. Citizen/Community Juries (CJs) are a form of public participation that aims to elicit an informed community perspective on controversial topics. Reporting standards for CJ processes have already been proposed. However, less clarity exists about the standards for what constitutes a good quality CJ deliberation—we aim to begin to address this gap here.
Methods
We identified the goals that underlie CJs and searched the literature to identify existing frameworks assessing the quality of CJ deliberations. We then mapped the items constituting these frameworks onto the CJ goals; where none of the frameworks addressed one of the CJ goals, we generated additional items that did map onto the goal.
Results
This yielded a single operationalized deductive coding framework, consisting of four deliberation elements and four recommendation elements. The deliberation elements focus on the following: jurors’ preferences and values, engagement with each other, referencing expert information and enrichment of the deliberation. The recommendation elements focus on the following: reaching a clear and identifiable recommendation, whether the recommendation directly addresses the CJ question, justification for the recommendation and adoption of societal (rather than individual) perspective. To explore the alignment between this framework and the goals underlying CJs, we mapped the operationalized framework onto the transcripts of a CJ.
Conclusion
Results suggest that framework items map well onto what transpires in an actual CJ deliberation. Further testing of the validity, generalizability and reliability of the framework is planned.
“…Applying the framework : The community jurors offered a wide range of reasons for their recommendation against case‐finding for dementia. These included the following: the absence of effective treatments, timing in the course of disease, impact of the results on the individual's mental and emotional health, and others . For example:Juror 10: “I see that to be diagnosed and told that you are destined to become a person with dementia, will be devastating for anyone.…”
Section: Resultsmentioning
confidence: 99%
“…Using the coding framework developed from stage 1, we piloted the application of the framework on transcripts of the jurors’ private deliberations during a recent community jury on case‐finding for dementia that was conducted in March 2017. We asked participants of this CJ “Should the health system encourage General Practitioners to practice ‘case‐finding’ of dementia in people older than 50?”.…”
Section: Methodsmentioning
confidence: 99%
“…The Jury consisted of 10 participants (five male and five female), whose average age was 62, and educational level was mixed, ranging from some secondary education to university postgraduate. Full details of that CJ have been reported elsewhere …”
Background
Public participation in health policy decision making is thought to improve the quality of the decisions and enhance their legitimacy. Citizen/Community Juries (CJs) are a form of public participation that aims to elicit an informed community perspective on controversial topics. Reporting standards for CJ processes have already been proposed. However, less clarity exists about the standards for what constitutes a good quality CJ deliberation—we aim to begin to address this gap here.
Methods
We identified the goals that underlie CJs and searched the literature to identify existing frameworks assessing the quality of CJ deliberations. We then mapped the items constituting these frameworks onto the CJ goals; where none of the frameworks addressed one of the CJ goals, we generated additional items that did map onto the goal.
Results
This yielded a single operationalized deductive coding framework, consisting of four deliberation elements and four recommendation elements. The deliberation elements focus on the following: jurors’ preferences and values, engagement with each other, referencing expert information and enrichment of the deliberation. The recommendation elements focus on the following: reaching a clear and identifiable recommendation, whether the recommendation directly addresses the CJ question, justification for the recommendation and adoption of societal (rather than individual) perspective. To explore the alignment between this framework and the goals underlying CJs, we mapped the operationalized framework onto the transcripts of a CJ.
Conclusion
Results suggest that framework items map well onto what transpires in an actual CJ deliberation. Further testing of the validity, generalizability and reliability of the framework is planned.
“…As such, they have been used for informing health policy on ethically complex topics, such as genetic testing, 15 screening services, 16 and case finding for stigmatized diseases. 17 Our research group previously organized 3citizens’ juries on health data sharing for research and commercialuse. 18–20 …”
Objective
To investigate how the general public trades off explainability versus accuracy of artificial intelligence (AI) systems and whether this differs between healthcare and non-healthcare scenarios.
Materials and Methods
Citizens’ juries are a form of deliberative democracy eliciting informed judgment from a representative sample of the general public around policy questions. We organized two 5-day citizens’ juries in the UK with 18 jurors each. Jurors considered 3 AI systems with different levels of accuracy and explainability in 2 healthcare and 2 non-healthcare scenarios. Per scenario, jurors voted for their preferred system; votes were analyzed descriptively. Qualitative data on considerations behind their preferences included transcribed audio-recordings of plenary sessions, observational field notes, outputs from small group work and free-text comments accompanying jurors’ votes; qualitative data were analyzed thematically by scenario, per and across AI systems.
Results
In healthcare scenarios, jurors favored accuracy over explainability, whereas in non-healthcare contexts they either valued explainability equally to, or more than, accuracy. Jurors’ considerations in favor of accuracy regarded the impact of decisions on individuals and society, and the potential to increase efficiency of services. Reasons for emphasizing explainability included increased opportunities for individuals and society to learn and improve future prospects and enhanced ability for humans to identify and resolve system biases.
Conclusion
Citizens may value explainability of AI systems in healthcare less than in non-healthcare domains and less than often assumed by professionals, especially when weighed against system accuracy. The public should therefore be actively consulted when developing policy on AI explainability.
“…The method assumes that people can think rationally, revise their opinions on the basis of evidence, and make defensible decisions when provided with appropriate conditions. Community juries have been used in Australia and elsewhere to consider, for example, the introduction of new health technologies and health resource prioritisation [60][61][62][63].…”
Background: Outbreaks of infectious disease cause serious and costly health and social problems. Two new technologiespathogen whole genome sequencing (WGS) and Big Data analyticspromise to improve our capacity to detect and control outbreaks earlier, saving lives and resources. However, routinely using these technologies to capture more detailed and specific personal information could be perceived as intrusive and a threat to privacy. Method: Four community juries were convened in two demographically different Sydney municipalities and two regional cities in New South Wales, Australia (western Sydney, Wollongong, Tamworth, eastern Sydney) to elicit the views of well-informed community members on the acceptability and legitimacy of:
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