123 results on '"Loe, Bao Sheng"'
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2. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
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Srivastava, Aarohi, Rastogi, Abhinav, Rao, Abhishek, Shoeb, Abu Awal Md, Abid, Abubakar, Fisch, Adam, Brown, Adam R., Santoro, Adam, Gupta, Aditya, Garriga-Alonso, Adrià, Kluska, Agnieszka, Lewkowycz, Aitor, Agarwal, Akshat, Power, Alethea, Ray, Alex, Warstadt, Alex, Kocurek, Alexander W., Safaya, Ali, Tazarv, Ali, Xiang, Alice, Parrish, Alicia, Nie, Allen, Hussain, Aman, Askell, Amanda, Dsouza, Amanda, Slone, Ambrose, Rahane, Ameet, Iyer, Anantharaman S., Andreassen, Anders, Madotto, Andrea, Santilli, Andrea, Stuhlmüller, Andreas, Dai, Andrew, La, Andrew, Lampinen, Andrew, Zou, Andy, Jiang, Angela, Chen, Angelica, Vuong, Anh, Gupta, Animesh, Gottardi, Anna, Norelli, Antonio, Venkatesh, Anu, Gholamidavoodi, Arash, Tabassum, Arfa, Menezes, Arul, Kirubarajan, Arun, Mullokandov, Asher, Sabharwal, Ashish, Herrick, Austin, Efrat, Avia, Erdem, Aykut, Karakaş, Ayla, Roberts, B. Ryan, Loe, Bao Sheng, Zoph, Barret, Bojanowski, Bartłomiej, Özyurt, Batuhan, Hedayatnia, Behnam, Neyshabur, Behnam, Inden, Benjamin, Stein, Benno, Ekmekci, Berk, Lin, Bill Yuchen, Howald, Blake, Orinion, Bryan, Diao, Cameron, Dour, Cameron, Stinson, Catherine, Argueta, Cedrick, Ramírez, César Ferri, Singh, Chandan, Rathkopf, Charles, Meng, Chenlin, Baral, Chitta, Wu, Chiyu, Callison-Burch, Chris, Waites, Chris, Voigt, Christian, Manning, Christopher D., Potts, Christopher, Ramirez, Cindy, Rivera, Clara E., Siro, Clemencia, Raffel, Colin, Ashcraft, Courtney, Garbacea, Cristina, Sileo, Damien, Garrette, Dan, Hendrycks, Dan, Kilman, Dan, Roth, Dan, Freeman, Daniel, Khashabi, Daniel, Levy, Daniel, González, Daniel Moseguí, Perszyk, Danielle, Hernandez, Danny, Chen, Danqi, Ippolito, Daphne, Gilboa, Dar, Dohan, David, Drakard, David, Jurgens, David, Datta, Debajyoti, Ganguli, Deep, Emelin, Denis, Kleyko, Denis, Yuret, Deniz, Chen, Derek, Tam, Derek, Hupkes, Dieuwke, Misra, Diganta, Buzan, Dilyar, Mollo, Dimitri Coelho, Yang, Diyi, Lee, Dong-Ho, Schrader, Dylan, Shutova, Ekaterina, Cubuk, Ekin Dogus, Segal, Elad, Hagerman, Eleanor, Barnes, Elizabeth, Donoway, Elizabeth, Pavlick, Ellie, Rodola, Emanuele, Lam, Emma, Chu, Eric, Tang, Eric, Erdem, Erkut, Chang, Ernie, Chi, Ethan A., Dyer, Ethan, Jerzak, Ethan, Kim, Ethan, Manyasi, Eunice Engefu, Zheltonozhskii, Evgenii, Xia, Fanyue, Siar, Fatemeh, Martínez-Plumed, Fernando, Happé, Francesca, Chollet, Francois, Rong, Frieda, Mishra, Gaurav, Winata, Genta Indra, de Melo, Gerard, Kruszewski, Germán, Parascandolo, Giambattista, Mariani, Giorgio, Wang, Gloria, Jaimovitch-López, Gonzalo, Betz, Gregor, Gur-Ari, Guy, Galijasevic, Hana, Kim, Hannah, Rashkin, Hannah, Hajishirzi, Hannaneh, Mehta, Harsh, Bogar, Hayden, Shevlin, Henry, Schütze, Hinrich, Yakura, Hiromu, Zhang, Hongming, Wong, Hugh Mee, Ng, Ian, Noble, Isaac, Jumelet, Jaap, Geissinger, Jack, Kernion, Jackson, Hilton, Jacob, Lee, Jaehoon, Fisac, Jaime Fernández, Simon, James B., Koppel, James, Zheng, James, Zou, James, Kocoń, Jan, Thompson, Jana, Wingfield, Janelle, Kaplan, Jared, Radom, Jarema, Sohl-Dickstein, Jascha, Phang, Jason, Wei, Jason, Yosinski, Jason, Novikova, Jekaterina, Bosscher, Jelle, Marsh, Jennifer, Kim, Jeremy, Taal, Jeroen, Engel, Jesse, Alabi, Jesujoba, Xu, Jiacheng, Song, Jiaming, Tang, Jillian, Waweru, Joan, Burden, John, Miller, John, Balis, John U., Batchelder, Jonathan, Berant, Jonathan, Frohberg, Jörg, Rozen, Jos, Hernandez-Orallo, Jose, Boudeman, Joseph, Guerr, Joseph, Jones, Joseph, Tenenbaum, Joshua B., Rule, Joshua S., Chua, Joyce, Kanclerz, Kamil, Livescu, Karen, Krauth, Karl, Gopalakrishnan, Karthik, Ignatyeva, Katerina, Markert, Katja, Dhole, Kaustubh D., Gimpel, Kevin, Omondi, Kevin, Mathewson, Kory, Chiafullo, Kristen, Shkaruta, Ksenia, Shridhar, Kumar, McDonell, Kyle, Richardson, Kyle, Reynolds, Laria, Gao, Leo, Zhang, Li, Dugan, Liam, Qin, Lianhui, Contreras-Ochando, Lidia, Morency, Louis-Philippe, Moschella, Luca, Lam, Lucas, Noble, Lucy, Schmidt, Ludwig, He, Luheng, Colón, Luis Oliveros, Metz, Luke, Şenel, Lütfi Kerem, Bosma, Maarten, Sap, Maarten, ter Hoeve, Maartje, Farooqi, Maheen, Faruqui, Manaal, Mazeika, Mantas, Baturan, Marco, Marelli, Marco, Maru, Marco, Quintana, Maria Jose Ramírez, Tolkiehn, Marie, Giulianelli, Mario, Lewis, Martha, Potthast, Martin, Leavitt, Matthew L., Hagen, Matthias, Schubert, Mátyás, Baitemirova, Medina Orduna, Arnaud, Melody, McElrath, Melvin, Yee, Michael A., Cohen, Michael, Gu, Michael, Ivanitskiy, Michael, Starritt, Michael, Strube, Michael, Swędrowski, Michał, Bevilacqua, Michele, Yasunaga, Michihiro, Kale, Mihir, Cain, Mike, Xu, Mimee, Suzgun, Mirac, Walker, Mitch, Tiwari, Mo, Bansal, Mohit, Aminnaseri, Moin, Geva, Mor, Gheini, Mozhdeh, T, Mukund Varma, Peng, Nanyun, Chi, Nathan A., Lee, Nayeon, Krakover, Neta Gur-Ari, Cameron, Nicholas, Roberts, Nicholas, Doiron, Nick, Martinez, Nicole, Nangia, Nikita, Deckers, Niklas, Muennighoff, Niklas, Keskar, Nitish Shirish, Iyer, Niveditha S., Constant, Noah, Fiedel, Noah, Wen, Nuan, Zhang, Oliver, Agha, Omar, Elbaghdadi, Omar, Levy, Omer, Evans, Owain, Casares, Pablo Antonio Moreno, Doshi, Parth, Fung, Pascale, Liang, Paul Pu, Vicol, Paul, Alipoormolabashi, Pegah, Liao, Peiyuan, Liang, Percy, Chang, Peter, Eckersley, Peter, Htut, Phu Mon, Hwang, Pinyu, Miłkowski, Piotr, Patil, Piyush, Pezeshkpour, Pouya, Oli, Priti, Mei, Qiaozhu, Lyu, Qing, Chen, Qinlang, Banjade, Rabin, Rudolph, Rachel Etta, Gabriel, Raefer, Habacker, Rahel, Risco, Ramon, Millière, Raphaël, Garg, Rhythm, Barnes, Richard, Saurous, Rif A., Arakawa, Riku, Raymaekers, Robbe, Frank, Robert, Sikand, Rohan, Novak, Roman, Sitelew, Roman, LeBras, Ronan, Liu, Rosanne, Jacobs, Rowan, Zhang, Rui, Salakhutdinov, Ruslan, Chi, Ryan, Lee, Ryan, Stovall, Ryan, Teehan, Ryan, Yang, Rylan, Singh, Sahib, Mohammad, Saif M., Anand, Sajant, Dillavou, Sam, Shleifer, Sam, Wiseman, Sam, Gruetter, Samuel, Bowman, Samuel R., Schoenholz, Samuel S., Han, Sanghyun, Kwatra, Sanjeev, Rous, Sarah A., Ghazarian, Sarik, Ghosh, Sayan, Casey, Sean, Bischoff, Sebastian, Gehrmann, Sebastian, Schuster, Sebastian, Sadeghi, Sepideh, Hamdan, Shadi, Zhou, Sharon, Srivastava, Shashank, Shi, Sherry, Singh, Shikhar, Asaadi, Shima, Gu, Shixiang Shane, Pachchigar, Shubh, Toshniwal, Shubham, Upadhyay, Shyam, Shyamolima, Debnath, Shakeri, Siamak, Thormeyer, Simon, Melzi, Simone, Reddy, Siva, Makini, Sneha Priscilla, Lee, Soo-Hwan, Torene, Spencer, Hatwar, Sriharsha, Dehaene, Stanislas, Divic, Stefan, Ermon, Stefano, Biderman, Stella, Lin, Stephanie, Prasad, Stephen, Piantadosi, Steven T., Shieber, Stuart M., Misherghi, Summer, Kiritchenko, Svetlana, Mishra, Swaroop, Linzen, Tal, Schuster, Tal, Li, Tao, Yu, Tao, Ali, Tariq, Hashimoto, Tatsu, Wu, Te-Lin, Desbordes, Théo, Rothschild, Theodore, Phan, Thomas, Wang, Tianle, Nkinyili, Tiberius, Schick, Timo, Kornev, Timofei, Tunduny, Titus, Gerstenberg, Tobias, Chang, Trenton, Neeraj, Trishala, Khot, Tushar, Shultz, Tyler, Shaham, Uri, Misra, Vedant, Demberg, Vera, Nyamai, Victoria, Raunak, Vikas, Ramasesh, Vinay, Prabhu, Vinay Uday, Padmakumar, Vishakh, Srikumar, Vivek, Fedus, William, Saunders, William, Zhang, William, Vossen, Wout, Ren, Xiang, Tong, Xiaoyu, Zhao, Xinran, Wu, Xinyi, Shen, Xudong, Yaghoobzadeh, Yadollah, Lakretz, Yair, Song, Yangqiu, Bahri, Yasaman, Choi, Yejin, Yang, Yichi, Hao, Yiding, Chen, Yifu, Belinkov, Yonatan, Hou, Yu, Hou, Yufang, Bai, Yuntao, Seid, Zachary, Zhao, Zhuoye, Wang, Zijian, Wang, Zijie J., Wang, Zirui, and Wu, Ziyi
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Computers and Society ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting., Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench
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- 2022
3. Assessing AI capabilities with education tests
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Staneva, Mila, primary, Baret, Abel, additional, Aso-Mollar, Ángel, additional, Blass, Joseph, additional, Carrión Ponz, Salvador, additional, Conitzer, Vincent, additional, Cortes, Ulises, additional, Dasigi, Pradeep, additional, de Paula, Angel, additional, Galindo, Carlos, additional, Gobert, Janice, additional, Gonzàlez, Jordi, additional, Heintz, Fredrik, additional, Hendler, Jim, additional, Hendrycks, Daniel, additional, Hunter, Lawrence, additional, Izquierdo-Domenech, Juan, additional, Juarez, Maria, additional, Juraco Frias, Aina, additional, Keren, Aviv, additional, Koncel-Kedziorski, Rik, additional, Leake, David, additional, Loe, Bao Sheng, additional, Martinez-Plumed, Fernando, additional, Martin-Hammond, Aqueasha, additional, Matuszek, Cynthia, additional, Mestre Gascón, Antoni, additional, Moreno, Jose Andres, additional, Nakos, Constantine, additional, Olson, Taylor, additional, Rose, Carolyn, additional, Sarvazyan, Areg Mikael, additional, Scassellati, Brian, additional, Schellaert, Wout, additional, Strannegård, Claes, additional, Tan, Neset, additional, Taniguchi, Tadahiro, additional, Vold, Karina, additional, and Wooldridge, Michael, additional
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- 2023
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4. Measuring glaucoma quality of life in an Asian population using item banking: psychometric evaluation and computerized adaptive testing simulations
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Fenwick, Eva K., Lim, Belicia, Man, Ryan E. K., Baskaran, Mani, Nongpiur, Monisha, Sng, Chelvin C. A., Iyer, Jayant Venkatramani, Husain, Rahat, Perera, Shamira, Wong, Tina, Low, Jin Rong, Loe, Bao Sheng, Huang, Olivia Shimin, Lun, Katherine, Aung, Tin, and Lamoureux, Ecosse L.
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- 2023
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5. A multidisciplinary task-based perspective for evaluating the impact of AI autonomy and generality on the future of work
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Fernández-Macías, Enrique, Gómez, Emilia, Hernández-Orallo, José, Loe, Bao Sheng, Martens, Bertin, Martínez-Plumed, Fernando, and Tolan, Songül
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Computer Science - Artificial Intelligence ,Computer Science - Computers and Society ,68T99 - Abstract
This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work. We provide definitions of a task from two main perspectives: socio-economic and computational. We propose to explore ways in which we can integrate or map these perspectives, and link them with the skills or capabilities required by them, for humans and AI systems. Finally, we argue that in order to understand the dynamics of tasks, we have to explore the relevance of autonomy and generality of AI systems for the automation or alteration of the workplace., Comment: AEGAP2018 Workshop at ICML 2018, 7 pages, 1 table
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- 2018
6. The effectiveness of automatic item generation for the development of cognitive ability tests
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Loe, Bao Sheng and Rust, John
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153.9 ,Intelligence ,Item Response Theory ,Automatic Item Generation ,Executive Functioning ,Factor Analysis ,Linear Logistic Test Models - Abstract
Research has shown that the increased use of computer-based testing has brought about new challenges. With the ease of online test administration, a large number of items are necessary to maintain the item bank and minimise the exposure rate. However, the traditional item development process is time-consuming and costly. Thus, alternative ways of creating items are necessary to improve the item development process. Automatic Item Generation (AIG) is an effective method in generating items rapidly and efficiently. AIG uses algorithms to create questions for testing purposes. However, many of these generators are in the closed form, available only to the selected few. There is a lack of open source, publicly available generators that researchers can utilise to study AIG in greater depth and to generate items for their research. Furthermore, research has indicated that AIG is far from being understood, and more research into its methodology and the psychometric properties of the items created by the generators are needed for it to be used effectively. The studies conducted in this thesis have achieved the following: 1) Five open source item generators were created, and the items were evaluated and validated. 2) Empirical evidence showed that using a weak theory approach to develop item generators was just as credible as using a strong theory approach, even though they are theoretically distinct. 3) The psychometric properties of the generated items were estimated using various IRT models to assess the impact of the template features used to create the items. 4) Joint responses and response time modelling was employed to provide new insights into cognitive processes that go beyond those obtained by typical IRT models. This thesis suggests that AIG provides a tangible solution for improving the item development process for content generation and reducing the procedural cost of generating a large number of items, with the possibility of a unified approach towards test administration (i.e. adaptive item generation). Nonetheless, this thesis focused on rule-based algorithms. The application of other forms of item generation methods and the potential for measuring the intelligence of artificial general intelligence (AGI) is discussed in the final chapter, proposing that the use of AIG techniques create new opportunities as well as challenges for researchers that will redefine the assessment of intelligence.
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- 2019
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7. Optimizing measurement of vision-related quality of life : a computerized adaptive test for the impact of vision impairment questionnaire (IVI-CAT)
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Fenwick, Eva K., Loe, Bao Sheng, Khadka, Jyoti, Man, Ryan E. K., Rees, Gwyn, and Lamoureux, Ecosse L.
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- 2020
8. Effects of different types of written vaccination information on COVID-19 vaccine hesitancy in the UK (OCEANS-III): a single-blind, parallel-group, randomised controlled trial
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Freeman, Daniel, Loe, Bao Sheng, Yu, Ly-Mee, Freeman, Jason, Chadwick, Andrew, Vaccari, Cristian, Shanyinde, Milensu, Harris, Victoria, Waite, Felicity, Rosebrock, Laina, Petit, Ariane, Vanderslott, Samantha, Lewandowsky, Stephan, Larkin, Michael, Innocenti, Stefania, Pollard, Andrew J, McShane, Helen, and Lambe, Sinéad
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- 2021
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9. Daydreaming and grandiose delusions: development of the Qualities of Daydreaming Scale
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Isham, Louise, primary, Loe, Bao Sheng, additional, Hicks, Alice, additional, Wilson, Natalie, additional, Bentall, Richard P., additional, and Freeman, Daniel, additional
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- 2024
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10. Efficiency, Precision, Validity, and Reliability of GlauCAT-Asian Computerized Adaptive Tests in Measuring Glaucoma-Related Quality of Life
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Fenwick, Eva K., primary, Man, Ryan E. K., additional, Lim, Belicia, additional, Baskaran, Mani, additional, Nongpiur, Monisha, additional, Sng, Chelvin C. A., additional, Iyer, Jayant Venkatramani, additional, Husain, Rahat, additional, Perera, Shamira, additional, Wong, Tina, additional, Low, Jin Rong, additional, Huang, Olivia Shimin, additional, Lun, Katherine, additional, Loe, Bao Sheng, additional, Aung, Tin, additional, and Lamoureux, Ecosse L., additional
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- 2024
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11. General intelligence disentangled via a generality metric for natural and artificial intelligence
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Hernández-Orallo, José, Loe, Bao Sheng, Cheke, Lucy, Martínez-Plumed, Fernando, and Ó hÉigeartaigh, Seán
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- 2021
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12. Validating the Unmind Index as a measure of mental health and wellbeing among adults in USA, Australia, and New Zealand
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Travers, Eoin, primary, Loe, Bao Sheng, additional, Sun, Luning, additional, and Bolton, Heather, additional
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- 2023
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13. Explaining paranoia: cognitive and social processes in the occurrence of extreme mistrust
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Freeman, Daniel, primary and Loe, Bao Sheng, additional
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- 2023
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14. Mapping Intelligence: Requirements and Possibilities
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Bhatnagar, Sankalp, Alexandrova, Anna, Avin, Shahar, Cave, Stephen, Cheke, Lucy, Crosby, Matthew, Feyereisl, Jan, Halina, Marta, Loe, Bao Sheng, Ó hÉigeartaigh, Seán, Martínez-Plumed, Fernando, Price, Huw, Shevlin, Henry, Weller, Adrian, Winfield, Alan, Hernández-Orallo, José, Magnani, Lorenzo, Series Editor, and Müller, Vincent C., editor
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- 2018
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15. Measuring dissociation across adolescence and adulthood: Developing the short-form Cernis Felt Sense of Anomaly scale (CEFSA-14)
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Černis, Emma, primary, Loe, Bao Sheng, additional, Lofthouse, Katie, additional, Waite, Polly, additional, Molodynski, Andrew, additional, Ehlers, Anke, additional, and Freeman, Daniel, additional
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- 2023
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16. The Difficulties of Grandiose Delusions: Harms, Challenges, and Implications for Treatment Engagement
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Isham, Louise, primary, Loe, Bao Sheng, additional, Hicks, Alice, additional, Wilson, Natalie, additional, Bentall, Richard P, additional, and Freeman, Daniel, additional
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- 2023
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17. Disease Influenced Vaccine Acceptance Scale-Six
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Grech, Lisa, primary, Loe, Bao Sheng, additional, Day, Daphne, additional, Freeman, Daniel, additional, Kwok, Alastair, additional, Nguyen, Mike, additional, Bain, Nathan, additional, and Segelov, Eva, additional
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- 2023
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18. The Disease Influenced Vaccine Acceptance Scale-Six (DIVAS-6): Validation of a Measure to Assess Disease-Related COVID-19 Vaccine Attitudes and Concerns.
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Grech, Lisa, Loe, Bao Sheng, Day, Daphne, Freeman, Daniel, Kwok, Alastair, Nguyen, Mike, Bain, Nathan, and Segelov, Eva
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VACCINATION , *MULTIPLE sclerosis , *RESEARCH , *STRUCTURAL equation modeling , *DISEASE progression , *COVID-19 , *ATTITUDE (Psychology) , *RESEARCH methodology evaluation , *COVID-19 vaccines , *RESEARCH methodology , *CHRONIC diseases , *ATTITUDES toward illness , *SEVERITY of illness index , *CANCER patients , *PSYCHOMETRICS , *QUESTIONNAIRES , *VACCINE hesitancy , *FACTOR analysis , *STATISTICAL correlation ,RESEARCH evaluation - Abstract
Patients with underlying comorbidities are particularly vulnerable to poor outcomes from SARS-CoV-2 infection. Despite the context-specific nature of vaccine hesitancy, there are currently no scales that incorporate disease or treatment-related hesitancy factors. We developed a six-item scale assessing disease-related COVID-19 vaccine attitudes and concerns (The Disease Influenced COVID-19 Vaccine Acceptance Scale-Six: DIVAS-6). A survey incorporating the DIVAS-6 was completed by 4683 participants with severe and/or chronic illness (3560 cancer; 842 diabetes; 281 multiple sclerosis (MS)). The survey included the Oxford COVID-19 Vaccine Hesitancy Scale, the Oxford COVID-19 Vaccine Confidence and Complacency Scale, demographic, disease-related, and vaccination status questions. The six items loaded onto two factors (disease complacency and vaccine vulnerability) using exploratory factor analysis and exploratory structural equation modeling. The two factors were internally consistent. Measurement invariance analysis showed the two factors displayed psychometric equivalence across the patient groups. Each factor significantly correlated with the two Oxford COVID-19 Vaccine scales, showing convergent validity. The summary score showed acceptable ability to discriminate vaccination status across diseases, with the total sample providing good-to-excellent discriminative ability. The DIVAS-6 has two factors measuring COVID-19 vaccine attitudes and concerns relating to potential complications of SARS-CoV-2 infection due to underlying disease (disease complacency) and vaccine-related impact on disease progression and treatment (vaccine vulnerability). This is the first validated scale to measure disease-related COVID-19 vaccine concerns and has been validated in people with cancer, diabetes, and MS. It is quick to administer and should assist with guiding information delivery about COVID-19 vaccination in medically vulnerable populations. [ABSTRACT FROM AUTHOR]
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- 2023
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19. COVID-19 Vaccine Hesitancy in Australian Patients with Solid Organ Cancers
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Bain, Nathan, Nguyen, Mike, Grech, Lisa, Day, Daphne, McCartney, Amelia, Webber, Kate, Kwok, Alastair, Harris, Sam, Chau, Hieu, Chan, Bryan, Nott, Louise, Hamad, Nada, Tognela, Annette, Underhill, Craig, Loe, Bao Sheng, Freeman, Daniel, Segelov, Eva, Investigators, On Behalf Of The Canvaccs, Day, Daphne [0000-0001-8262-2441], McCartney, Amelia [0000-0003-0411-0736], Webber, Kate [0000-0003-4350-0884], Hamad, Nada [0000-0001-7929-1450], Underhill, Craig [0000-0001-9335-2678], Freeman, Daniel [0000-0002-2541-2197], Segelov, Eva [0000-0002-4410-6144], and Apollo - University of Cambridge Repository
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COVID-19 ,vaccine hesitancy ,cancer ,vaccination - Abstract
BACKGROUND: Vaccination is the cornerstone of the global public health response to the COVID-19 pandemic. Excess morbidity and mortality of COVID-19 infection is seen in people with cancer. COVID-19 vaccine hesitancy has been observed in this medically vulnerable population, although associated attitudes and beliefs remain poorly understood. METHODS: An online cross-sectional survey of people with solid organ cancers was conducted through nine health services across Australia. Demographics, cancer-related characteristics and vaccine uptake were collected. Perceptions and beliefs regarding COVID-19 vaccination were assessed using the Oxford COVID-19 Vaccine Hesitancy Scale, the Oxford COVID-19 Vaccine Confidence and Complacency Scale and the Disease Influenced Vaccine Acceptance Scale-6. RESULTS: Between June and October 2021, 2691 people with solid organ cancers completed the survey. The median age was 62.5 years (SD = 11.8; range 19-95), 40.9% were male, 71.3% lived in metropolitan areas and 90.3% spoke English as their first language. The commonest cancer diagnoses were breast (36.6%), genitourinary (18.6%) and gastrointestinal (18.3%); 59.2% had localized disease and 56.0% were receiving anti-cancer therapy. Most participants (79.7%) had at least one COVID-19 vaccine dose. Vaccine uptake was higher in people who were older, male, metropolitan, spoke English as a first language and had a cancer diagnosis for more than six months. Vaccine hesitancy was higher in people who were younger, female, spoke English as a non-dominant language and lived in a regional location, and lower in people with genitourinary cancer. Vaccinated respondents were more concerned about being infected with COVID-19 and less concerned about vaccine safety and efficacy. CONCLUSIONS: People with cancer have concerns about acquiring COVID-19, which they balance against vaccine-related concerns about the potential impact on their disease progress and/or treatment. Detailed exploration of concerns in cancer patients provides valuable insights, both for discussions with individual patients and public health messaging for this vulnerable population.
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- 2022
20. Serious Underlying Medical Conditions and COVID-19 Vaccine Hesitancy: A Large Cross-Sectional Analysis from Australia
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Day, Daphne, Grech, Lisa, Nguyen, Mike, Bain, Nathan, Kwok, Alastair, Harris, Sam, Chau, Hieu, Chan, Bryan, Blennerhassett, Richard, Nott, Louise, Hamad, Nada, Tognela, Annette, Hoffman, David, McCartney, Amelia, Webber, Kate, Wong, Jennifer, Underhill, Craig, Sillars, Brett, Winkel, Antony, Savage, Mark, Loe, Bao Sheng, Freeman, Daniel, Segelov, Eva, Diabvaccs, On Behalf Of The Canvaccs, Day, Daphne [0000-0001-8262-2441], Nguyen, Mike [0000-0003-3044-1707], Bain, Nathan [0000-0002-3320-8444], McCartney, Amelia [0000-0003-0411-0736], Webber, Kate [0000-0003-4350-0884], Winkel, Antony [0000-0002-6100-4764], Segelov, Eva [0000-0002-4410-6144], and Apollo - University of Cambridge Repository
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Pharmacology ,COVID-19 ,vaccine hesitancy ,cancer ,diabetes ,multiple sclerosis ,Infectious Diseases ,Drug Discovery ,Immunology ,Pharmacology (medical) - Abstract
As COVID-19 vaccinations became available and were proven effective in preventing serious infection, uptake amongst individuals varied, including in medically vulnerable populations. This cross-sectional multi-site study examined vaccine uptake, hesitancy, and explanatory factors amongst people with serious and/or chronic health conditions, including the impact of underlying disease on attitudes to vaccination. A 42-item survey was distributed to people with cancer, diabetes, or multiple sclerosis across ten Australian health services from 30 June to 5 October 2021. The survey evaluated sociodemographic and disease-related characteristics and incorporated three validated scales measuring vaccine hesitancy and vaccine-related beliefs generally and specific to their disease: the Oxford COVID-19 Vaccine Hesitancy Scale, the Oxford COVID-19 Vaccine Confidence and Complacency Scale and the Disease Influenced Vaccine Acceptance Scale-Six. Among 4683 participants (2548 [54.4%] female, 2108 [45.0%] male, 27 [0.6%] other; mean [SD] age, 60.6 [13.3] years; 3560 [76.0%] cancer, 842 [18.0%] diabetes, and 281 [6.0%] multiple sclerosis), 3813 (81.5%) self-reported having at least one COVID-19 vaccine. Unvaccinated status was associated with younger age, female sex, lower education and income, English as a second language, and residence in regional areas. Unvaccinated participants were more likely to report greater vaccine hesitancy and more negative perceptions toward vaccines. Disease-related vaccine concerns were associated with unvaccinated status and hesitancy, including greater complacency about COVID-19 infection, and concerns relating to vaccine efficacy and impact on their disease and/or treatment. This highlights the need to develop targeted strategies and education about COVID-19 vaccination to support medically vulnerable populations and health professionals.
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- 2022
21. Virtual reality (VR) therapy for patients with psychosis: satisfaction and side effects.
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Freeman, Daniel, Rosebrock, Laina, Waite, Felicity, Loe, Bao Sheng, Kabir, Thomas, Petit, Ariane, Dudley, Robert, Chapman, Kate, Morrison, Anthony, O'Regan, Eileen, Aynsworth, Charlotte, Jones, Julia, Murphy, Elizabeth, Powling, Rosie, Peel, Heather, Walker, Harry, Byrne, Rory, Freeman, Jason, Rovira, Aitor, and Galal, Ushma
- Subjects
SCHIZOPHRENIA treatment ,PSYCHOSES ,VIRTUAL reality therapy ,EXPOSURE therapy ,PATIENT satisfaction ,BEHAVIOR therapy ,HEALTH outcome assessment ,TREATMENT effectiveness ,PATIENTS' attitudes ,QUESTIONNAIRES ,DESCRIPTIVE statistics ,RESEARCH funding ,MENTAL health services - Abstract
Background: Automated virtual reality therapies are being developed to increase access to psychological interventions. We assessed the experience with one such therapy of patients diagnosed with psychosis, including satisfaction, side effects, and positive experiences of access to the technology. We tested whether side effects affected therapy. Methods: In a clinical trial 122 patients diagnosed with psychosis completed baseline measures of psychiatric symptoms, received gameChange VR therapy, and then completed a satisfaction questionnaire, the Oxford-VR Side Effects Checklist, and outcome measures. Results: 79 (65.8%) patients were very satisfied with VR therapy, 37 (30.8%) were mostly satisfied, 3 (2.5%) were indifferent/mildly dissatisfied, and 1 (0.8%) person was quite dissatisfied. The most common side effects were: difficulties concentrating because of thinking about what might be happening in the room (n = 17, 14.2%); lasting headache (n = 10, 8.3%); and the headset causing feelings of panic (n = 9, 7.4%). Side effects formed three factors: difficulties concentrating when wearing a headset, feelings of panic using VR, and worries following VR. The occurrence of side effects was not associated with number of VR sessions, therapy outcomes, or psychiatric symptoms. Difficulties concentrating in VR were associated with slightly lower satisfaction. VR therapy provision and engagement made patients feel: proud (n = 99, 81.8%); valued (n = 97, 80.2%); and optimistic (n = 96, 79.3%). Conclusions: Patients with psychosis were generally very positive towards the VR therapy, valued having the opportunity to try the technology, and experienced few adverse effects. Side effects did not significantly impact VR therapy. Patient experience of VR is likely to facilitate widespread adoption. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
22. How General-Purpose Is a Language Model? Usefulness and Safety with Human Prompters in the Wild
- Author
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Casares, Pablo Antonio Moreno, primary, Loe, Bao Sheng, additional, Burden, John, additional, HEigeartaigh, Sean, additional, and Hernández-Orallo, José, additional
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- 2022
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23. Validating the Unmind Index as a measure of mental health and wellbeing among adults in USA, Australia, and New Zealand
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Travers, Eoin, primary, Loe, Bao Sheng, additional, Sun, Luning, additional, and Bolton, Heather, additional
- Published
- 2022
- Full Text
- View/download PDF
24. The Disease Influenced Vaccine Acceptance Scale-Six (DIVAS-6): Validation of a Measure to Assess Disease-Related COVID-19 Vaccine Attitudes and Concerns
- Author
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Grech, Lisa, primary, Loe, Bao Sheng, additional, Day, Daphne, additional, Freeman, Daniel, additional, Kwok, Alastair, additional, Nguyen, Mike, additional, Bain, Nathan, additional, and Segelov, Eva, additional
- Published
- 2022
- Full Text
- View/download PDF
25. Virtual reality (VR) therapy for patients with psychosis: satisfaction and side effects
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Freeman, Daniel, primary, Rosebrock, Laina, additional, Waite, Felicity, additional, Loe, Bao Sheng, additional, Kabir, Thomas, additional, Petit, Ariane, additional, Dudley, Robert, additional, Chapman, Kate, additional, Morrison, Anthony, additional, O'Regan, Eileen, additional, Aynsworth, Charlotte, additional, Jones, Julia, additional, Murphy, Elizabeth, additional, Powling, Rosie, additional, Peel, Heather, additional, Walker, Harry, additional, Byrne, Rory, additional, Freeman, Jason, additional, Rovira, Aitor, additional, Galal, Ushma, additional, Yu, Ly-Mee, additional, Clark, David M., additional, and Lambe, Sinéad, additional
- Published
- 2022
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- View/download PDF
26. Serious underlying medical conditions and COVID-19 vaccine hesitancy
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Day, Daphne, primary, Grech, Lisa, additional, Nguyen, Mike, additional, Bain, Nathan, additional, Kwok, Alastair, additional, Harris, Sam, additional, Chau, Hieu, additional, Chan, Bryan, additional, Blennerhassett, Richard, additional, Nott, Louise, additional, Hamad, Nada, additional, Tognela, Annette, additional, Hoffman, David, additional, McCartney, Amelia, additional, Webber, Kate, additional, Wong, Jennifer, additional, Underhill, Craig, additional, Sillars, Brett, additional, Winkel, Antony, additional, Savage, Mark, additional, Loe, Bao Sheng, additional, Freeman, Daniel, additional, and Segelov, Eva, additional
- Published
- 2022
- Full Text
- View/download PDF
27. The Oxford Agoraphobic Avoidance Scale.
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Lambe, Sinead, Bird, Jessica C., Loe, Bao Sheng, Rosebrock, Laina, Kabir, Thomas, Petit, Ariane, Mulhall, Sophie, Jenner, Lucy, Aynsworth, Charlotte, Murphy, Elizabeth, Jones, Julia, Powling, Rosie, Chapman, Kate, Dudley, Robert, Morrison, Anthony, Regan, Eileen O., Yu, Ly-Mee, Clark, David, Waite, Felicity, and Freeman, Daniel
- Subjects
EXPERIMENTAL design ,STATISTICAL reliability ,RESEARCH evaluation ,PSYCHOSES ,RESEARCH methodology ,RESEARCH methodology evaluation ,SELF-evaluation ,ACTIGRAPHY ,AGORAPHOBIA ,AVOIDANCE (Psychology) ,PSYCHOMETRICS ,FACTOR analysis ,RESEARCH funding ,ANXIETY ,RECEIVER operating characteristic curves ,PSYCHOLOGICAL distress - Abstract
Background: Agoraphobic avoidance of everyday situations is a common feature in many mental health disorders. Avoidance can be due to a variety of fears, including concerns about negative social evaluation, panicking, and harm from others. The result is inactivity and isolation. Behavioural avoidance tasks (BATs) provide an objective assessment of avoidance and in situ anxiety but are challenging to administer and lack standardisation. Our aim was to draw on the principles of BATs to develop a self-report measure of agoraphobia symptoms. Method: The scale was developed with 194 patients with agoraphobia in the context of psychosis, 427 individuals in the general population with high levels of agoraphobia, and 1094 individuals with low levels of agoraphobia. Factor analysis, item response theory, and receiver operating characteristic analyses were used. Validity was assessed against a BAT, actigraphy data, and an existing agoraphobia measure. Test–retest reliability was assessed with 264 participants. Results: An eight-item questionnaire with avoidance and distress response scales was developed. The avoidance and distress scales each had an excellent model fit and reliably assessed agoraphobic symptoms across the severity spectrum. All items were highly discriminative (avoidance: a = 1.24–5.43; distress: a = 1.60–5.48), indicating that small increases in agoraphobic symptoms led to a high probability of item endorsement. The scale demonstrated good internal reliability, test–retest reliability, and validity. Conclusions: The Oxford Agoraphobic Avoidance Scale has excellent psychometric properties. Clinical cut-offs and score ranges are provided. This precise assessment tool may help focus attention on the clinically important problem of agoraphobic avoidance. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
28. Injection fears and COVID-19 vaccine hesitancy.
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Freeman, Daniel, Lambe, Sinéad, Yu, Ly-Mee, Freeman, Jason, Chadwick, Andrew, Vaccari, Cristian, Waite, Felicity, Rosebrock, Laina, Petit, Ariane, Vanderslott, Samantha, Lewandowsky, Stephan, Larkin, Michael, Innocenti, Stefania, McShane, Helen, Pollard, Andrew J., and Loe, Bao Sheng
- Subjects
VACCINATION ,THERAPEUTICS ,INJECTIONS ,CONFIDENCE intervals ,ATTITUDE (Psychology) ,INTERNET ,AGE distribution ,BLACK people ,FEAR ,MEDICAL screening ,RACE ,INCOME ,SURVEYS ,VACCINE hesitancy ,DESCRIPTIVE statistics ,ODDS ratio - Abstract
Background: When vaccination depends on injection, it is plausible that the blood-injection-injury cluster of fears may contribute to hesitancy. Our primary aim was to estimate in the UK adult population the proportion of COVID-19 vaccine hesitancy explained by blood-injection-injury fears. Methods: In total, 15 014 UK adults, quota sampled to match the population for age, gender, ethnicity, income and region, took part (19 January–5 February 2021) in a non-probability online survey. The Oxford COVID-19 Vaccine Hesitancy Scale assessed intent to be vaccinated. Two scales (Specific Phobia Scale-blood-injection-injury phobia and Medical Fear Survey–injections and blood subscale) assessed blood-injection-injury fears. Four items from these scales were used to create a factor score specifically for injection fears. Results: In total, 3927 (26.2%) screened positive for blood-injection-injury phobia. Individuals screening positive (22.0%) were more likely to report COVID-19 vaccine hesitancy compared to individuals screening negative (11.5%), odds ratio = 2.18, 95% confidence interval (CI) 1.97–2.40, p < 0.001. The population attributable fraction (PAF) indicated that if blood-injection-injury phobia were absent then this may prevent 11.5% of all instances of vaccine hesitancy, AF = 0.11; 95% CI 0.09–0.14, p < 0.001. COVID-19 vaccine hesitancy was associated with higher scores on the Specific Phobia Scale, r = 0.22, p < 0.001, Medical Fear Survey, r = 0.23, p = <0.001 and injection fears, r = 0.25, p < 0.001. Injection fears were higher in youth and in Black and Asian ethnic groups, and explained a small degree of why vaccine hesitancy is higher in these groups. Conclusions: Across the adult population, blood-injection-injury fears may explain approximately 10% of cases of COVID-19 vaccine hesitancy. Addressing such fears will likely improve the effectiveness of vaccination programmes. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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29. Unmind Index
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Sierk, Anika, primary, Travers, Eoin, additional, Economides, Marcos, additional, Loe, Bao Sheng, additional, Sun, Luning, additional, and Bolton, Heather, additional
- Published
- 2022
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30. The Meaning in Grandiose Delusions
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Isham, Louise, primary, Loe, Bao Sheng, additional, Hicks, Alice, additional, Wilson, Natalie, additional, Bentall, Richard P., additional, Bird, Jessica C., additional, and Freeman, Daniel, additional
- Published
- 2022
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31. The Unmind Index: development and UK validation of a new digital assessment of mental health and wellbeing (Preprint)
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Sierk, Anika, primary, Travers, Eoin, additional, Economides, Marcos, additional, Loe, Bao Sheng, additional, Sun, Luning, additional, and Bolton, Heather, additional
- Published
- 2021
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32. A New Digital Assessment of Mental Health and Well-being in the Workplace: Development and Validation of the Unmind Index (Preprint)
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Sierk, Anika, primary, Travers, Eoin, additional, Economides, Marcos, additional, Loe, Bao Sheng, additional, Sun, Luning, additional, and Bolton, Heather, additional
- Published
- 2021
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33. Modern Test Theory Techniques for Adaptive Testing in Short Scales Comprising Polytomous Items: A Monte Carlo Simulation Study Comparing Rasch Measurement Theory to Unidimensional and Multidimensional Graded Response Models. (Preprint)
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Harrison, Conrad J., primary, Loe, Bao Sheng, additional, Apon, Inge, additional, Sidey-Gibbons, Chris J., additional, Swan, Marc C., additional, Furniss, Dominic, additional, Klassen, Anne F., additional, Wong Riff, Karen W. Y., additional, Versnel, Sarah L., additional, Koudstaal, Maarten J., additional, Allori, Alexander C., additional, Rogers-Vizena, Carolyn R., additional, and Rodrigues, Jeremy N., additional
- Published
- 2021
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34. The Oxford Agoraphobic Avoidance Scale
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Lambe, Sinead, primary, Bird, Jessica C., additional, Loe, Bao Sheng, additional, Rosebrock, Laina, additional, Kabir, Thomas, additional, Petit, Ariane, additional, Mulhall, Sophie, additional, Jenner, Lucy, additional, Aynsworth, Charlotte, additional, Murphy, Elizabeth, additional, Jones, Julia, additional, Powling, Rosie, additional, Chapman, Kate, additional, Dudley, Robert, additional, Morrison, Anthony, additional, Regan, Eileen O., additional, Yu, Ly-Mee, additional, Clark, David, additional, Waite, Felicity, additional, and Freeman, Daniel, additional
- Published
- 2021
- Full Text
- View/download PDF
35. Injection fears and COVID-19 vaccine hesitancy
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Freeman, Daniel, primary, Lambe, Sinéad, additional, Yu, Ly-Mee, additional, Freeman, Jason, additional, Chadwick, Andrew, additional, Vaccari, Cristian, additional, Waite, Felicity, additional, Rosebrock, Laina, additional, Petit, Ariane, additional, Vanderslott, Samantha, additional, Lewandowsky, Stephan, additional, Larkin, Michael, additional, Innocenti, Stefania, additional, McShane, Helen, additional, Pollard, Andrew J., additional, and Loe, Bao Sheng, additional
- Published
- 2021
- Full Text
- View/download PDF
36. Effects of different types of written vaccination information on COVID-19 vaccine hesitancy in the UK (OCEANS-III):a single-blind, parallel-group, randomised controlled trial
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Freeman, Daniel, Loe, Bao Sheng, Yu, Ly-mee, Freeman, Jason, Chadwick, Andrew, Vaccari, Cristian, Shanyinde, Milensu, Harris, Victoria, Waite, Felicity, Rosebrock, Laina, Petit, Ariane, Vanderslott, Samantha, Lewandowsky, Stephan, Larkin, Michael, Innocenti, Stefania, Pollard, Andrew J, Mcshane, Helen, Lambe, Sinéad, Freeman, Daniel, Loe, Bao Sheng, Yu, Ly-mee, Freeman, Jason, Chadwick, Andrew, Vaccari, Cristian, Shanyinde, Milensu, Harris, Victoria, Waite, Felicity, Rosebrock, Laina, Petit, Ariane, Vanderslott, Samantha, Lewandowsky, Stephan, Larkin, Michael, Innocenti, Stefania, Pollard, Andrew J, Mcshane, Helen, and Lambe, Sinéad
- Abstract
BACKGROUND: The effectiveness of the COVID-19 vaccination programme depends on mass participation: the greater the number of people vaccinated, the less risk to the population. Concise, persuasive messaging is crucial, particularly given substantial levels of vaccine hesitancy in the UK. Our aim was to test which types of written information about COVID-19 vaccination, in addition to a statement of efficacy and safety, might increase vaccine acceptance. METHODS: For this single-blind, parallel-group, randomised controlled trial, we aimed to recruit 15 000 adults in the UK, who were quota sampled to be representative. Participants were randomly assigned equally across ten information conditions stratified by level of vaccine acceptance (willing, doubtful, or strongly hesitant). The control information condition comprised the safety and effectiveness statement taken from the UK National Health Service website; the remaining conditions addressed collective benefit, personal benefit, seriousness of the pandemic, and safety concerns. After online provision of vaccination information, participants completed the Oxford COVID-19 Vaccine Hesitancy Scale (outcome measure; score range 7-35) and the Oxford Vaccine Confidence and Complacency Scale (mediation measure). The primary outcome was willingness to be vaccinated. Participants were analysed in the groups they were allocated. p values were adjusted for multiple comparisons. The study was registered with ISRCTN, ISRCTN37254291. FINDINGS: From Jan 19 to Feb 5, 2021, 15 014 adults were recruited. Vaccine hesitancy had reduced from 26·9% the previous year to 16·9%, so recruitment was extended to Feb 18 to recruit 3841 additional vaccine-hesitant adults. 12 463 (66·1%) participants were classified as willing, 2932 (15·6%) as doubtful, and 3460 (18·4%) as strongly hesitant (ie, report that they will avoid being vaccinated for as long as possible or will never get vaccinated). Information conditions did not alter COVID-19 vaccine
- Published
- 2021
37. Injection fears and COVID-19 vaccine hesitancy
- Author
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Freeman, Daniel, Lambe, Sinéad, Yu, Ly-Mee, Freeman, Jason, Chadwick, Andrew, Vaccari, Cristian, Waite, Felicity, Rosebrock, Laina, Petit, Ariane, Vanderslott, Samantha, Lewandowsky, Stephan, Larkin, Michael, Innocenti, Stefania, McShane, Helen, Pollard, Andrew J, Loe, Bao Sheng, Freeman, Daniel, Lambe, Sinéad, Yu, Ly-Mee, Freeman, Jason, Chadwick, Andrew, Vaccari, Cristian, Waite, Felicity, Rosebrock, Laina, Petit, Ariane, Vanderslott, Samantha, Lewandowsky, Stephan, Larkin, Michael, Innocenti, Stefania, McShane, Helen, Pollard, Andrew J, and Loe, Bao Sheng
- Abstract
Background: When vaccination depends on injection, it is plausible that the blood-injection-injury cluster of fears may contribute to hesitancy. Our primary aim was to estimate in the UK adult population the proportion of COVID-19 vaccine hesitancy explained by blood-injection-injury fears. Methods: 15,014 UK adults, quota sampled to match the population for age, gender, ethnicity, income, and region, took part (19th January-5th February 2021) in a non-probability online survey. The Oxford COVID-19 Vaccine Hesitancy Scale assessed intent to be vaccinated. Two scales (Specific Phobia Scale-blood-injection-injury phobia; Medical Fear Survey-injections and blood subscale) assessed blood-injection-injury fears. Four items from these scales were used to create a factor score specifically for injection fears. Results: 3927 (26.2%) screened positive for blood-injection-injury phobia. Individuals screening positive (22.0%) were more likely to report COVID-19 vaccine hesitancy than individuals screening negative (11.5%), odds ratio=2.18, CI: 1.97-2.40, p <.001. The population attributable fraction indicated that if blood-injection-injury phobia were absent then this may prevent 11.5% of all instances of vaccine hesitancy, AF=0.11; 95% CI: 0.09-0.14, p < 0.001. COVID-19 vaccine hesitancy was associated with higher scores on the Specific Phobia Scale, r=0.22, p <.001, Medical Fear Survey, r=0.23, p= <.001, and injection fears, r=0.25, p <.001. Injection fears were higher in youth and in Black and Asian ethnic groups, and explained a small degree of why vaccine hesitancy is higher in these groups. Conclusions: Across the adult population, blood-injection-injury fears may explain approximately 10% of cases of COVID-19 vaccine hesitancy. Addressing such fears will likely improve the effectiveness of vaccination programmes.
- Published
- 2021
38. General intelligence disentangled via a generality metric for natural and artificial intelligence
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Leverhulme Trust, European Commission, Generalitat Valenciana, Future of Life Institute, U.S. Department of Defense, Agencia Estatal de Investigación, European Regional Development Fund, COMISION DE LAS COMUNIDADES EUROPEA, Hernández-Orallo, José, Loe, Bao Sheng, Cheke, Lucy, Martínez-Plumed, Fernando, Heigeartaigh, Sean O., Leverhulme Trust, European Commission, Generalitat Valenciana, Future of Life Institute, U.S. Department of Defense, Agencia Estatal de Investigación, European Regional Development Fund, COMISION DE LAS COMUNIDADES EUROPEA, Hernández-Orallo, José, Loe, Bao Sheng, Cheke, Lucy, Martínez-Plumed, Fernando, and Heigeartaigh, Sean O.
- Abstract
[EN] Success in all sorts of situations is the most classical interpretation of general intelligence. Under limited resources, however, the capability of an agent must necessarily be limited too, and generality needs to be understood as comprehensive performance up to a level of difficulty. The degree of generality then refers to the way an agent's capability is distributed as a function of task difficulty. This dissects the notion of general intelligence into two non-populational measures, generality and capability, which we apply to individuals and groups of humans, other animals and AI systems, on several cognitive and perceptual tests. Our results indicate that generality and capability can decouple at the individual level: very specialised agents can show high capability and vice versa. The metrics also decouple at the population level, and we rarely see diminishing returns in generality for those groups of high capability. We relate the individual measure of generality to traditional notions of general intelligence and cognitive efficiency in humans, collectives, non-human animals and machines. The choice of the difficulty function now plays a prominent role in this new conception of generality, which brings a quantitative tool for shedding light on long-standing questions about the evolution of general intelligence and the evaluation of progress in Artificial General Intelligence.
- Published
- 2021
39. Computerized Adaptive Tests: Efficient and Precise Assessment of the Patient-Centered Impact of Diabetic Retinopathy
- Author
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Fenwick, Eva K., primary, Barnard, John, additional, Gan, Alfred, additional, Loe, Bao Sheng, additional, Khadka, Jyoti, additional, Pesudovs, Konrad, additional, Man, Ryan, additional, Lee, Shu Yen, additional, Tan, Gavin, additional, Wong, Tien Y., additional, and Lamoureux, Ecosse L., additional
- Published
- 2020
- Full Text
- View/download PDF
40. Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning (Preprint)
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Harrison, Conrad, primary, Loe, Bao Sheng, additional, Lis, Przemysław, additional, and Sidey-Gibbons, Chris, additional
- Published
- 2020
- Full Text
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41. Paranoia and unusual sensory experiences in Parkinson’s disease.
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Brown, Poppy, Freeman, Daniel, Loe, Bao Sheng, Dow, Rebecca, and Johns, Louise
- Abstract
AbstractObjectivesMethodResultsConclusionThere has been limited exploration into the nature and development of psychotic experiences (PEs) in Parkinson’s disease (PD). We aimed to comprehensively assess the frequency, severity, and associated distress of paranoia and unusual sensory experiences (USEs) in PD, and to assess what variables are significantly associated with these experiences, focussing on psychological processes central to understanding PEs in non-PD groups.A questionnaire battery was completed by 369 individuals with PD with a mean age of 66 years and mean time since diagnosis of 5 years. Recruitment was
via Parkinson’s UK, social media, and local community groups. For a subset of measures, comparisons were made to age-matched controls using pre-existing data.182 (49%) participants reported USEs, including almost half of those not taking dopaminergic medication. For 83 (23%), the experience was distressing. Paranoia across the sample was significantly lower than in age-matched controls. However, specific paranoid concerns around abandonment (16%) and spousal betrayal (10%) were reported by some. Depression, anxiety, loneliness, and stigma and desire for support with PEs were high across the sample. Almost all psychological variables were significantly associated with PEs in structural equation models.PEs in PD are common, even in those not taking dopaminergic medication. For a small subset, these experiences are distressing and not resolved by existing treatment. Cognitive-affective variables like depression and anxiety could play a maintaining role in PEs in PD thus providing easy avenues for trialling intervention. [ABSTRACT FROM AUTHOR]- Published
- 2025
- Full Text
- View/download PDF
42. Spatial Network Test
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Loe, Bao Sheng, primary
- Published
- 2020
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43. A multidisciplinary task-based perspective for evaluating the impact of AI autonomy and generality on the future of work
- Author
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Fern��ndez-Mac��as, Enrique, G��mez, Emilia, Hern��ndez-Orallo, Jos��, Loe, Bao Sheng, Martens, Bertin, Mart��nez-Plumed, Fernando, and Tolan, Song��l
- Subjects
FOS: Computer and information sciences ,Computer Science - Computers and Society ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Computers and Society (cs.CY) ,68T99 - Abstract
This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work. We provide definitions of a task from two main perspectives: socio-economic and computational. We propose to explore ways in which we can integrate or map these perspectives, and link them with the skills or capabilities required by them, for humans and AI systems. Finally, we argue that in order to understand the dynamics of tasks, we have to explore the relevance of autonomy and generality of AI systems for the automation or alteration of the workplace., AEGAP2018 Workshop at ICML 2018, 7 pages, 1 table
- Published
- 2018
44. Optimizing measurement of vision-related quality of life: a computerized adaptive test for the impact of vision impairment questionnaire (IVI-CAT)
- Author
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Fenwick, Eva K., primary, Loe, Bao Sheng, additional, Khadka, Jyoti, additional, Man, Ryan E. K., additional, Rees, Gwyn, additional, and Lamoureux, Ecosse L., additional
- Published
- 2019
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45. The assessment of paranoia in young people: Item and test properties of the Bird Checklist of Adolescent Paranoia
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Bird, Jessica Charlotte, primary, Loe, Bao Sheng, additional, Kirkham, Miriam, additional, Fergusson, Emma C, additional, Shearn, Christina, additional, Stratford, Hannah, additional, Teale, Ashley, additional, Waite, Felicity, additional, and Freeman, Daniel, additional
- Published
- 2019
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46. The Development and Validation of an Online Spatial Network Measure
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Loe, Bao Sheng, primary
- Published
- 2019
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47. The Development and Validation of an Online Spatial Network Measure.
- Author
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Loe, Bao Sheng
- Subjects
- *
FACTOR analysis , *INTELLECT , *RESEARCH methodology evaluation , *EXECUTIVE function ,RESEARCH evaluation - Abstract
Planning is an essential cognitive process of executive functions and is considered as one of the most important brain functions. Planning has been extensively studied in the field of neuropsychology, but there is a lack of computerized assessment tools of planning ability that are easily accessible to researchers and clinicians. The present study aims to validate a newly developed online spatial network test that is designed for both clinical and nonclinical populations. Exploratory factor analysis revealed a unidimensional factorial structure with moderate to high internal consistency in the test. Two-parameter logistic item response theory analysis showed acceptable item and model fit and no violation of the local independence assumption. The overall success rate exhibited a positive correlation with the performance of planning before attempting to solve the items. After correcting for attenuation, moderate to high correlations were found between the spatial network test and the International Cognitive Ability Resource 16 short form cognitive ability test and the automated perceptual maze test, respectively, demonstrating convergent, discriminant, and criterion validity. Future directions and potential applications of the spatial network test as an assessment tool to measure planning for researchers and clinicians are discussed in the end. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
48. Development of the Knowledge of Genome Sequencing (KOGS) questionnaire
- Author
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Sanderson, Saskia C., primary, Loe, Bao Sheng, additional, Freeman, Maddie, additional, Gabriel, Camila, additional, Stevenson, Danielle C., additional, Gibbons, Chris, additional, Chitty, Lyn, additional, and Lewis, Celine, additional
- Published
- 2018
- Full Text
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49. The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI
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Martínez-Plumed, Fernando, primary, Loe, Bao Sheng, additional, Flach, Peter, additional, Ó hÉigeartaigh, Seán, additional, Vold, Karina, additional, and Hernández-Orallo, José, additional
- Published
- 2018
- Full Text
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50. Opening the Black Box: A practitioner’s guide to Artificial Intelligence and Machine Learning in assessment (Part 2)
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Abrahams, Mark, primary, Loe, Bao Sheng (Aiden), additional, and Riley, Philippa, additional
- Published
- 2018
- Full Text
- View/download PDF
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