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Developing artificial Intelligence (AI) technology has become a business of power. AI innovation is increasingly centralized in a few large companies – mainly, Google, Facebook, and Apple.1 Specialized data scientists - the backbone of these institutions - understand how AI functions, further creating a power dynamic between the layperson and the corporatized specialist. In the age of COVID-19, we have all become more reliant on technology companies and innovations to fulfill the needs of our new digital lives. We have little choice but to trust them in developing the AI technologies of the future. non-peer-reviewed

Student Summer Research Awards, University College Dublin, Ireland, 28 September 2021 An abundance of literature is being published reporting the negative mental health sequelae of the COVID-19 pandemic. This surge in mental health problems will likely present to primary care over the coming months. Initiatives are being proposed nationally and internationally to tackle this problem. It is of utmost importance for general practitioners to have interventions in place which can improve care of these mental illnesses. This research aims to undertake a scoping review of the literature to examine interventions which could be implemented in general practice post COVID-19 to improve care of mental health disorders arising from the pandemic.

From March 2020, the Covid-19 pandemic revealed the inherent vulnerability of our modern society to emerging infectious diseases. Globalisation of products and high travel frequency contributed to the rapid spread of infection, and particularly in densely populated urban areas.

Data files related to the manuscript Perspectives and experiences of Covid-19: Two Irish studies of families in disadvantaged communities. The manuscript includes two studies. The following materials are shared below. Study 1: - Qualitative data (Microsoft Office Excel file) - Codebook for coding the qualitative data developed through content analysis (pdf file) Study 2: - Qualitative data (Microsoft Office Excel file) Data are named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file. Both studies in the manuscript were developed by the Childhood Development Initiative (CDI), Dublin, Ireland. Study 1 was conducted within the project PEAR_EC, that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925. Study 2 was conducted within the Child Poverty research project, funded by Tusla under the Area Based Childhood funding and the Child and Youth Participation Initiatives grant.

Canadian singer-songwriter Joni Mitchell's 1970’s hit "Big Yellow Taxi" had the immortal lines: "don’t it always seem to go/That you don’t know what you got/'Til its gone". With our favourite watering holes absent from our lives since mid-March - and perhaps some not reopening after a five-month hiatus until mid-August - these lyrics may resonate with many in how much we have taken our pubs for granted. When regular pub-goers do take their first tentative steps towards a socially distanced rendezvous with their local pub, it’s a reunion that may be quietly savoured by some, but one that’s postponed by others, punters and publicans alike.

The 6th Workshop on Noisy User-generated Text (W-NUT 2020), Virtual Workshop,19 November 2020 In this paper, we describe our approach in the shared task: COVID-19 event extraction from Twitter. The objective of this task is to extract answers from COVID-related tweets to a set of predefined slot-filling questions. Our approach treats the event extraction task as a question answering task by leveraging the transformer-based T5 text-to-text model. According to the official evaluation scores returned, namely F1, our submitted run achieves competitive performance compared to other participating runs (Top 3). However, we argue that this evaluation may underestimate the actual performance of runs based on text-generation. Although some such runs may answer the slot questions well, they may not be an exact string match for the gold standard answers. To measure the extent of this underestimation, we adopt a simple exact-answer transformation method aiming at converting the well-answered predictions to exactly-matched predictions. The results show that after this transformation our run overall reaches the same level of performance as the best participating run and state-of-the-art F1 scores in three of five COVID-related events. Our code is publicly available to aid reproducibility