title: COVIDiSTRESS diverse dataset on psychological and behavioural outcomes one year into the COVID-19 pandemic

authors: Blackburn, Angélique M.

abstract: During the onset of the COVID-19 pandemic, the COVIDiSTRESS Consortium launched an open-access global survey to understand and improve individuals’ experiences related to the crisis. A year later, we extended this line of research by launching a new survey to address the dynamic landscape of the pandemic. This survey was released with the goal of addressing diversity, equity, and inclusion by working with over 150 researchers across the globe who collected data in 48 languages and dialects across 137 countries. The resulting cleaned dataset described here includes 15,740 of over 20,000 responses. The dataset allows cross-cultural study of psychological wellbeing and behaviours a year into the pandemic. It includes measures of stress, resilience, vaccine attitudes, trust in government and scientists, compliance, and information acquisition and misperceptions regarding COVID-19. Open-access raw and cleaned datasets with computed scores are available. Just as our initial COVIDiSTRESS dataset has facilitated government policy decisions regarding health crises, this dataset can be used by researchers and policy makers to inform research, decisions, and policy.</jats:p>

text: COVIDiSTRESS diverse dataset on psychological and behavioural outcomes one year into the COVID-19 pandemic During the onset of the COVID-19 pandemic, the COVIDiSTRESS Consortium launched an open-access global survey to understand and improve individuals’ experiences related to the crisis. A year later, we extended this line of research by launching a new survey to address the dynamic landscape of the pandemic. This survey was released with the goal of addressing diversity, equity, and inclusion by working with over 150 researchers across the globe who collected data in 48 languages and dialects across 137 countries. The resulting cleaned dataset described here includes 15,740 of over 20,000 responses. The dataset allows cross-cultural study of psychological wellbeing and behaviours a year into the pandemic. It includes measures of stress, resilience, vaccine attitudes, trust in government and scientists, compliance, and information acquisition and misperceptions regarding COVID-19. Open-access raw and cleaned datasets with computed scores are available. Just as our initial COVIDiSTRESS dataset has facilitated government policy decisions regarding health crises, this dataset can be used by researchers and policy makers to inform research, decisions, and policy.</jats:p>

year: 2022

title: Eliciting Expert Uncertainty from Decision Making

authors: Falconer, Julia Ruth

subjects: bayesian

abstract: Eliciting expert uncertainty is a complex task with many caveats. Its usefulness in high-level decision-making and Bayesian inference makes it a necessary task that must be completed accurately. This thesis explores uncertainty elicitation in terms of prior elicitation for Bayesian inference and the wider knowledge elicitation  field. It describes methods currently available for prior elicitation and proposes a new typology, highlighting lesser-known methods. Based on the limitations of current methods, a new method for prior elicitation is introduced. This new method allows an analyst to model expert decision-making data to elicit a probability distribution that reflects expert uncertainty. An example of parole board decision-making is used to elicit a prior distribution of a prisoner re-offending upon release from prison. This example shows analysts how to elicit distributions for tabular data; however, more complex data types, such as images and reports, are often used for decision-making. To elicit distributions capturing expert uncertainty from more complex data, this thesis introduces a deep learning approach and uses an example of eliciting cancer risk, in histopathology, to illustrate this approach for the wider knowledge elicitation field.

text: Eliciting Expert Uncertainty from Decision Making Eliciting expert uncertainty is a complex task with many caveats. Its usefulness in high-level decision-making and Bayesian inference makes it a necessary task that must be completed accurately. This thesis explores uncertainty elicitation in terms of prior elicitation for Bayesian inference and the wider knowledge elicitation  field. It describes methods currently available for prior elicitation and proposes a new typology, highlighting lesser-known methods. Based on the limitations of current methods, a new method for prior elicitation is introduced. This new method allows an analyst to model expert decision-making data to elicit a probability distribution that reflects expert uncertainty. An example of parole board decision-making is used to elicit a prior distribution of a prisoner re-offending upon release from prison. This example shows analysts how to elicit distributions for tabular data; however, more complex data types, such as images and reports, are often used for decision-making. To elicit distributions capturing expert uncertainty from more complex data, this thesis introduces a deep learning approach and uses an example of eliciting cancer risk, in histopathology, to illustrate this approach for the wider knowledge elicitation field.

year: 2024

title: The COVID-19 domestic vaccine pass: Implications for Māori

authors: Kukutai, Tahu

abstract: The New Zealand government has introduced a COVID-19 domestic vaccine pass to be used in conjunction with the COVID-19 Protection Framework. The Framework is likely to be activated soon after Cabinet meets on 29 November 2021. The pass will be necessary to access places and events that require proof of vaccination under the Framework.
This brief does not argue for or against the introduction of a domestic vaccine pass, but rather discusses key issues that it raises for Māori, and suggests actions to address them. We see four key issues:
• the lack of Māori involvement, as a Tiriti partner, on either the design or implementation of the COVID-19 domestic vaccine pass;
• disproportionate restriction on Māori mobility due to lower Māori vaccination rates;
• privacy and data security concerns;
• uneven implementation that could increase discrimination against Māori and other groups considered to pose a risk to others’ safety.
To respond to these issues we recommend that the implementation of the pass be designed in partnership with Māori and comply with Māori data sovereignty requirements. As Tiriti partners, Māori should expect that the pass will keep their communities safe, while providing opportunities to enact manaakitanga in the matrix of care, and the mana to manage their own affairs.

text: The COVID-19 domestic vaccine pass: Implications for Māori The New Zealand government has introduced a COVID-19 domestic vaccine pass to be used in conjunction with the COVID-19 Protection Framework. The Framework is likely to be activated soon after Cabinet meets on 29 November 2021. The pass will be necessary to access places and events that require proof of vaccination under the Framework.
This brief does not argue for or against the introduction of a domestic vaccine pass, but rather discusses key issues that it raises for Māori, and suggests actions to address them. We see four key issues:
• the lack of Māori involvement, as a Tiriti partner, on either the design or implementation of the COVID-19 domestic vaccine pass;
• disproportionate restriction on Māori mobility due to lower Māori vaccination rates;
• privacy and data security concerns;
• uneven implementation that could increase discrimination against Māori and other groups considered to pose a risk to others’ safety.
To respond to these issues we recommend that the implementation of the pass be designed in partnership with Māori and comply with Māori data sovereignty requirements. As Tiriti partners, Māori should expect that the pass will keep their communities safe, while providing opportunities to enact manaakitanga in the matrix of care, and the mana to manage their own affairs.

year: 2021-11-30