From policy to practice: prioritizing person-centred healthcare actions in the state of Victoria

Background Meaningful involvement of consumers in healthcare is a high priority worldwide. In Victoria, Australia, a Partnering in Healthcare (PiH) policy framework was developed to guide health services in addressing consumer-focused healthcare improvements. The aim of this project was to identify priorities for improvement relating to the framework from the perspective of Victorian healthcare consumers and those who work in the healthcare sector. Methods A survey of Victorians representing key stakeholder groups was used to identify a “long list” of potential priorities, followed by a day-long summit to reduce this to a “short list” using explicit prioritization criteria. The survey was piloted prior to implementation, and diverse consumer groups and key health service providers were purposefully sampled for the summit. Results The survey (n = 680 respondents) generated 14–20 thematic categories across the proposed framework’s five domains. The summit (n = 31 participants, including n = 21 consumer representatives) prioritized the following five areas based on the survey findings: communication, shared decision-making, (shared) care planning, health (system) literacy and people (not) around the patient. These priorities were underpinned by three cross-cutting principles: care/compassion/respect, accountability and diversity. Conclusion Few studies have explicitly sought consumer input on health policy implementation. Adopting a codesign approach enabled the framework to be a shared foundation of healthcare improvement. The framework was subsequently launched in 2019. All Victorian health services are required to commit annually to improvement priorities against at least two framework domains.

Engagement Detection Framework for Hand Gesture and Posture Recognition

Hand gesture and posture recognition play an important role in Human-Computer Interaction (HCI) applications. They are main attributes in object or environment manipulations using vision-based inter- faces. However, before interpreting these gestures and postures as operational activities, a meaningful involvement with the target object should be detected. This meaningful involvement is called engagement. Upper-body posture gives significant information about user engagement. In this research, for our first contribution, a novel multi-modal model for engagement detection, called Disengagement, Attention, Intention, Action (DAIA) framework is presented. Disengagement happens when the user is disengaged from the target object. Attention occurs when user pays attention to the target, but doesn't have the intention to take any actions. In Intention state, the user intends to perform an action, but still does not. Action state is when the user is performing an action with hand. Using DAIA, the spectrum of mental status for performing a manipulative action is quantized in a finite number of engagement states. The second contribution of this research is in designing multiple binary classifiers based on upper-body postures for state detection. 3D skeleton data is extracted from depth image and is used to extract body posture information. Combining the output of all binary classifiers in an order makes engagement feature vector. Moreover, This feature vector could be extended using other channels of biometric information such as voice or gaze. However the engagemnet classifiers recognize the state change with acceptable accuracy, minor changes in body postures or false detection of joint locations for some milliseconds may result in transition to another states. For removing this unwanted noise and increasing the accuracy of the system, an Finite State Machine (FSM) is designed based on the properties of human activities. The design of Engagement FSM is our third major contribution. Finally, rotation matrix is used to increase the number of samples for training the deep learning classifier for hand posture recognition.

Engagement Detection Framework for Hand Gesture and Posture Recognition

Hand gesture and posture recognition play an important role in Human-Computer Interaction (HCI) applications. They are main attributes in object or environment manipulations using vision-based inter- faces. However, before interpreting these gestures and postures as operational activities, a meaningful involvement with the target object should be detected. This meaningful involvement is called engagement. Upper-body posture gives significant information about user engagement. In this research, for our first contribution, a novel multi-modal model for engagement detection, called Disengagement, Attention, Intention, Action (DAIA) framework is presented. Disengagement happens when the user is disengaged from the target object. Attention occurs when user pays attention to the target, but doesn't have the intention to take any actions. In Intention state, the user intends to perform an action, but still does not. Action state is when the user is performing an action with hand. Using DAIA, the spectrum of mental status for performing a manipulative action is quantized in a finite number of engagement states. The second contribution of this research is in designing multiple binary classifiers based on upper-body postures for state detection. 3D skeleton data is extracted from depth image and is used to extract body posture information. Combining the output of all binary classifiers in an order makes engagement feature vector. Moreover, This feature vector could be extended using other channels of biometric information such as voice or gaze. However the engagemnet classifiers recognize the state change with acceptable accuracy, minor changes in body postures or false detection of joint locations for some milliseconds may result in transition to another states. For removing this unwanted noise and increasing the accuracy of the system, an Finite State Machine (FSM) is designed based on the properties of human activities. The design of Engagement FSM is our third major contribution. Finally, rotation matrix is used to increase the number of samples for training the deep learning classifier for hand posture recognition.

Decision-making in the birth space

Susann Huschke discusses how communicating with birthing people in the current technocratic maternity systems in Ireland and elsewhere can inhibit the birthing person's meaningful involvement in decision-making

The data archive as factory: Alienation and resistance of data processors

Archival data processing consists of cleaning and formatting data between the moment a dataset is deposited and its publication on the archive’s website. In this article, I approach data processing by combining scholarship on invisible labor in knowledge infrastructures with a Marxian framework and show the relevance of considering data processing as factory labor. Using this perspective to analyze ethnographic data collected during a six-month participatory observation at a U.S. data archive, I generate a taxonomy of the forms of alienation that data processing generates, but also the types of resistance that processors develop, across four categories: routine, speed, skill, and meaning. This synthetic approach demonstrates, first, that data processing reproduces typical forms of factory worker’s alienation: processors are asked to work along a strict standardized pipeline, at a fast pace, without acquiring substantive skills or having a meaningful involvement in their work. It reveals, second, how data processors resist the alienating nature of this workflow by developing multiple tactics along the same four categories. Seen through this dual lens, data processors are therefore not only invisible workers, but also factory workers who follow and subvert a workflow organized as an assembly line. I conclude by proposing a four-step framework to better value the social contribution of data workers beyond the archive.

Social Contracts: Pillars of Community Conservation Partnerships in Lore Lindu National Park, Indonesia

The Community Conservation Partnership Agreement (KKM) was an effort to reduce, prevent and mitigate the impacts arising from the complexity of managing Lore Lindu National Park. Several approaches in building KKM in the National Park had been carried out by several parties but had not proceeded as expected. Social Contracts were built to advance community agreements. The purpose of this study was to explore the obstacles and strategies for implementing KKM in the National Park. A qualitative approach was used in this study, through in-depth interviews, field observations, and active research in the process of drafting the KKM agreement. The results showed there were multiple interpretations of the roles, functions, and work of the parties based on their authority and interests in building the KKM. This resulted in the KKM becoming unsustainable. Findings show that in order to re-establish the KKM requires strategic steps, which mediate across stakeholder interests. Partnerships towards effective social contracts would only succeed if there was recognition of, and meaningful involvement among parties that begin at the design and planning processes and continue throughout the implementation phases of the partnership activities. The process of building a social contract must therefore begin with solid communication between stakeholders, which establish institutional mechanisms that are systematic, promote active coordinative, and are based on the trust and understanding between stakeholders.