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5. Demonstrative Evidence and the Use of Algorithms in Jury Trials

Author

Rogers, Rachel and VanderPlas, Susan

Subjects
Computer Science - Computers and Society
Abstract

We investigate how the use of bullet comparison algorithms and demonstrative evidence may affect juror perceptions of reliability, credibility, and understanding of expert witnesses and presented evidence. The use of statistical methods in forensic science is motivated by a lack of scientific validity and error rate issues present in many forensic analysis methods. We explore what our study says about how this type of forensic evidence is perceived in the courtroom where individuals unfamiliar with advanced statistical methods are asked to evaluate results in order to assess guilt. In the course of our initial study, we found that individuals overwhelmingly provided high Likert scale ratings in reliability, credibility, and scientificity regardless of experimental condition. This discovery of scale compression - where responses are limited to a few values on a larger scale, despite experimental manipulations - limits statistical modeling but provides opportunities for new experimental manipulations which may improve future studies in this area.

Published
2023
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7. Tranexamic acid versus placebo to prevent bleeding in patients with haematological malignancies and severe thrombocytopenia (TREATT): a randomised, double-blind, parallel, phase 3 superiority trial

Author

Estcourt, Lise J, McQuilten, Zoe K, Bardy, Peter, Cole-Sinclair, Merrole, Collins, Graham P., Crispin, Philip J., Curnow, Elinor, Curnow, Jennifer, Degelia, Amber, Dyer, Claire, Friebe, Adam, Floro, Lajos, Grand, Effie, Hudson, Cara, Jones, Gail, Joseph, Joanne, Kallmeyer, Charlotte, Karakantza, Marina, Kerr, Paul, Last, Sara, Lobo-Clarke, Maria, Lumley, Matthew, McMullin, Mary F, Medd, Patrick G., Morton, Suzy M., Mumford, Andrew D., Mushkbar, Maria, Parsons, Joseph, Powter, Gillian, Sekhar, Mallika, Smith, Laura, Soutar, Richard, Stevenson, William S., Subramoniapillai, Elango, Szer, Jeff, Thomas, Helen, Waters, Neil A., Wei, Andrew H., Westerman, David A., Wexler, Sarah A., Wood, Erica M., Stanworth, Simon J., Abioye, Adrienne, Afghan, Rabia, Ai, Sylvia Ai, Akanni, Magbor, Alajangi, Rajesh, Alam, Usmaan, Al-Bubseeree, Bahaa, Alderson, Sophie, Alderson, Craig, Ali, Sayed, Ali, Kabir, Alighan, Rookmeen, Allam, Rebecca Allam, Allen, Tania, Al-Sakkaf, Wesam, Ames, Kate, Anderson, Jacqueline, Andrews, Colin, Angel, Ann-Marie, Anlya, Manuela Anlya, Ansari, Farah, Appleby, Rowan, Arnold, Claire, Asbjornsdottir, Hulda, Asfaw, Biruk, Atkins, Elissa, Atkinson, Leela, Aubrey, Clare, Ayesha, Noor, Babbola, Lola, Badcock, David, Badcock, Samuel, Baggio, Diva, Bailiff, Ben, Baines, Kizzy, Baker, Holly, Baker, Victoria, Ball, Lindsay, Ball, Martin, Balquin, Irwin, Banks, Emma, Banos, George, Barnett, Jaytee, Barrie, Claire, Barron, Claire, Barton, Rebecca, Bason, Nina, Batta, Bindu, Bautista, Dianne, Bayley, Angela, Bayly, Emma, Baynes, Fionnuala, Bazargan, Ali, Bazeley, Rachel, Beadle, Yvonne, Beardsmore, Claire, Beattie, Kate, Bedford, Caroline, Behal, Rachna, Behan, Daniel, Bejan, Lilihna, Bell, Sarah, Bell, Karen, Bell, Louise, Bell, Kaitlyn, Benjamin, Reuben, Bennett, Sam, Benson, Gary, Benson, Warwick, Bent, Cameron, Bergin, Krystal, Berry, Alex, Besenyei, Stephanie, Besley, Caroline, Betteridge, Scott, Beveridge, Leigh, Bhattacharyya, Abir, Billen, Annelies, Bilmon, Ian, Binns, Emma, Birt, Mark, Bishop, David, Blanco, Andrea, Bleby, Lisa, Blemnerhet, Richard, Blombery, Piers, Blyth, Emily, Blythe, Nicola, Boal, Lauren, Boden, Ali, Bokhari, Syed W.I., Bongetti, Elisa, Booth, Stephen, Borley, Jayne, Bowen, David, Bowers, Dawn, Boyd, Stephen, Bradley, Sarah, Bradman, Helen, Bretag, Peta, Brillante, Maria, Brockbank, Rachel, Brough, Yasmin, Brown, Ellen, Brown, Jo, Brown, Eleanor, Brown, Claire, Brown, Jenny, Brown, Susan, Browning, Joe, Brownsdon, Alex, Bruce, David, Brydon-Hill, Ruth, Buckwell, Andrea, Burgess, Dannielle, Burke, Glenda, Burley, Kate, Burney, Claire, Burns, David, Burrows, Samuel, burton, Kieran, Butler, Jason, Cambalova, Lenka, Camozzi, Maria C., Campbell, Philip, Campfield, Karen, Campion, Victoria, Cargo, Catherine, Carmona, Julia, Carney, Dennis, Casan, Joshua, Cashman, Helen, Catt, Lorraine, Cattell, Michael, Cavill, Megan, Chadbone, Rachel, Chaganti, Sridhar, Chai, Yee, Chai, Khai Li, Chang, Joshua, Chapman, Judith, Chapman, Oliver G., Chapter, Tamika, Charlton, Andrew, Chau, Celina, Chauhan, Saleena, Chavda, Nikesh, Chen, Frederick, Chen, Melody, Chen, Meng Xi, Chen, Melanie, Chen, Melissa, Cheok, Kathleen, Cheung, Mai, Chidgey, Luke, Chmielokliec, Karolina, Choi, Philip, Choi, Jae, Chok, Anne, Chopra, Ruchika, Christopherson, Louise, Chu, Vicky, Chua, Chong Chyn, Chudakou, Pavel, Chugh, Vidushi, Chung, Chi, Clark, Erin, Clarke, Peter, Clarke, Kathleen, Clay, Jennifer, Clayton, Laura, Clements, Mitch, Clemmens, Jonathan, Clifford, Ruth, Collett, Dave, Collins, Maia, Collyer, Emily, Connolly, Maureen, Cook, Mark, Coombs, Sarah, Coppell, Jason, Cornwell, Sophie, Corrigan, Claire, Coughlin, Elizabeth, Couling, Jennifer, Cousins, Tony, Cowan, Catriona, Cox, Christine, Cox, Catherine, Coyle, Luke, Craig, Emily, Creasey, Thomas, Croan, Laura, Croft, Jane, Crosbie, Nicola, Crowe, Josephine, Crowther, Helen, Crozier, Jane, Culleton, Naomi, Cullis, Jonathan, Cumming, Anita, Cummins, Michelle, Cunningham, Adam, Curley, Cameron, Curtis, Samantha, Cuthbert, Robert, Cuthill, Kirsty, Dahahayake, Dinusha A, Dang, Amy, Davies, Marc, Davies, Ceri, Dawson, Emily, Day, Tom, De Abrew, Kanchana, De Lavallade, Hugues, De Silva, Neelaskshi, Dean, Georgina, Deane, Christopher, Demosthenous, Lisa, Desai, Amisha, Desborough, Michael, Devanny, Ian, Dhanapal, Jay, Dhani, Sundip, Di Martino, Vicky, Dickens, Emmy, DiCorleto, Carmen, Dinnett, Louise, Dirisan, Divya, Dixon, Karen, Dixon, Kiri, Doal, Inderjit, Dobivh, J, Docanto, Maria, Doecke, Helve, Donaldson, David, Donaldson, Kylee, Donohoe, Carrie, Douglas, Ashley, Doung, Stephen, Downer, Susan, D'Rozario, James, Drummond, Malcolm, Drummond, Mark, Drummond, Samantha, Drysdale, Elizabeth, D'Souza, Ross, D'Souza, Eugene, Dunn, Alex, Dutton, David, Dyson, Martin, Ediriwicurena, Kushani, Edleston, Sharon, Edwards, Dawn, Edwards, Morgan, Edwards, Anita, Eise, Nicole, Ellis, Steven, Ellis, Hayley, Elmonley, Shareef, Enstone, Rosemarie, Eordogh, Agnes, Erb, Sharon, Evans, Shannon, Evans, Megan, Ewing, Joanne, Eyre, Toby, Facey, Adam, Fammy, Mina, Farman, Jon, Farnell, Rachel, Favero, Laura, Fay, Keith, Ferguson, Karen, Fernon, Laura, Filshie, Robin, Finnegan, Damian, Fisher, Lisa, Flanagan, Asia, Fleck, Emma, Fletcher, Simon, Flora, Harpreet, Flower, Catherine, Fodor, Ioana, Foley, Heather, Folland, Emma, Folorunso, Comfort, Forbes, Molly, Fordwor, Katrina, Foster, Polly, Fox, Vanessa, Fox, Thomas, Francis, Olesya, Fryearson, Louise, Fuery, Madonna, Fung, Jiin, Furtado, Michelle, Galloway-Browne, Leanne, Gamble, Louise, Gamgee, Jeanette, Ganapathy, Arundathi, Gardner, Hayley, Gardner, Clare, Gasmelsheed, Noha, Gately, Amy, Gaynor, Lynda, Gebreid, Alex, Geffens, Ruth, George, Rachel, Gertner, Aniko, Ghebeh, Manar, Ghirardini, Emanuela, Giddings, Melainie, Gillett, Sandra, Gillett, Karen, Giri, Pratyush, Glass, Chris, Glewis, Sarah, Gooding, Sarah, Gordon, Olivia, Gordon, Joanne, Gottlieb, David, Gowda, Koushik, Gower, Elysie, Gray, Nicola, Grayer, Jo, Greaves, Elaine, Greenaway, Sally Anne, Greenfield, Graeme, Greenwood, Matthew, Gregory, Gareth, Griffin, James, Griffith, Julia, Griffith, James, Griffiths, Lindsey, Grzegrzolka, Paulina, Gu, Yisu, Guest, Jo, Guinai, Rosanna, Gullapalli, Veena, Gunolr, A., Guo, Lina, H, Wayne, Hagua, Sophia, Haile, Senait, Hall, Richard, Hamdollah-Zadeh, Maryam, Hanif, Zahra, Hanlon, Kathleen, Hann, Nicholas, Hanna, Ramez, Hannah, Guy, Hapuarachchi, Sameera, Hardman, Jacinta, Hardy, Alison, Harris, Anthony, Harris, Kylie, Harrison, Beth, Harrison, Simon, Harrison, Lea-Anne, Harrop, Sean, Harvey, Caroline, Hatcliffe, Faye, Hawking, Jo, Hawkins, Matthew, Hayden, Janet, Hayman, Michelle, Haynes, Elizabeth, Heaney, Nicholas, Hebbard, Andrew, Hempton, Jenny, Hendunneti, Sasanka, Henry, Maeve, Heywood, Jonathan, Hildyard, Catherine, Hill, Lydia, Hilldrith, Annette, Hitev, Petar, Hiwase, Smita, Hiwase, Devendra, Hoare, Chris, Hodge, Renate, Holloway, Amy, Holt, Chloe, Holton, Kelly, Homer, Lauren, Horne, Gillian, Horvath, Noemi, Hotong, Linda, Houdyk, Kristen, Houseman, Katy, Hoxhallari, Ilda, Hsu, Hannah, Hsu, Nina, Huang, Gillian, Hudson, Kerryn, Hufton, Melanie, Hughes, Timothy, Hughes, Siobhan, Hurley, Kate, Huxley, Rosie, Ibitoye, Temitope, Ibrouf, Abubaken, Inam, Farha, Indran, Tishya, Ingham, Karen, Innes, Calum, Irvine, David, Jaafar, Sarah, Jain, Manish, Jameson, Laura, Janjua, Pardeep, Jarvis, Rebecca, Jatheendran, Abirami, Javed, Abbie, Jen, Sheila, Jobanpura, Shailesh, Jobson, Irene, John, Deborah, Johns, Sophie, Johnston, Amanda, Jones, Hollie, Jones, Francesca, Joniak, Karolina, Jovanovic, Michael, Jovic, Anita, Joyce, Lauren, Judd, Andrew, Kakarlamudi, Sudhakar, Kakaroubas, Nick, Kalita, Maggie, Kam, Shirly, Kan, Julie, Kandle, P, Kanellopoulos, Alex, Kao, Chien, Kaparou, Maria, Kartsios, Charamlampos, Katsioulas, Vicki, Kaye, Russell, Keen, Katie, Kelly, Richard, Kelly, Pauline, Kelly, Donna, Kelly, Melanie, Kennedy, Glen, Kennedy, Nola, Kenny, Angela, Kenworthy, Zoe, Kerridge, Ian, Kesavan, Murali, Khafizi, Angelika, Khakwani, Muhammad, Khalid, Amna, Khamly, Kate, Khan, Anjum, Khan, Dalia, Khan, Mojid, Khan, Lubna, Khoo, Mona, Khwaja, Asim, Kim, Grace, King, Andrew, King, Vicky, King, Donna, Kinsella, Francesca, Kipp, David, Kirandeep, Pachoo, Kirui, Laura C., Kishore, Bhuvan, Knectlhi, Christopher, Knot, Amy, Knot, Armit, Ko, Cathy, Kolaric, Caitlin, Koo, Ray, Kotadia, Mary, Kothari, Jaimal, Kottaridis, Panagiotis D., Kuiluinathan, Gajan, Kulasekararaj, Austin, Kwan, John, Kwok, Marwan, Kwok, Phillip, Kwok, Fiona, Laane, Kristiina, Lad, Deena, Laird, Jennifer, Lam, Ada, Lane, Mary, Lanenco, Monica, Lang, Susan, Langridge, Alex, Langton, Catherine, Lannon, Michelle, Latif, Annie, Latimer, Maya, Latter, Ruth, Lau, I-Jun, Lawless, Sarah, Lawless, Theresa, Leach, Mike, Leaney, Sarah, Leary, Heather, Leavy, James, LeBlanc, Abbey, Lee, Vivienne, Lee, Edwin, Lee, Jenny, Lee, Tamara, Leischkie, Marian, Leitinger, Emma, Leon, Christopher, Leonard, Jayne, Lewis, David, Lewis, Ian, Lewis, Tania, Lim, Daniel, Littlewood, Kelly, Liu, Dara, Loh, Joanna, Lokare, Anand, Lomas, Oliver, Lovell, Richard, Lowe, Theresa, Lowry, Lisa, Lubowiecki, Marcin, Lumb, Rebecca, Lynch, Gail, Macaulay, Amanda, MacDonald, Lyndsey, MacDonald-Burn, Jill, Macmillan, Margaret, Maddock, Karen, Mahaliyana, Tomas, Mahon, Cassandra, Maidment, Alison, Maier, Susie, Mairos, Michelle, Majid, Mahseeman, Mak, Ka L, Mak, Anne, Malendrayogau, Arunthrthy, Malham, Hana, Malyon, Felicity, Mandadapu, Vineela, Mandel, Laura, Mant, Sarah, Manton, Ruth, Maouche, Nadjoua, Maqbool, Muhammad G., Marchant, Gregory, Marinho, Mariana, Marks, David, Marner, Mike, Marr, Helen, Marshall, Gillian, Martin, Siobhan, Martin, Abigail, Marzolini, Maria, Mason, Kiara, Massie, Jonathan, Masson, Rebecca, Mathavan, Vidya, Mathew, Siju, Mathie, Judith, Mattocks, Lehenta, Maybury, Bernard, Mayer, Georgina, McAlister, Chyrelle, McAllister, Jo, McConnell, Stewart, McCracken, James, McCullagh, Liz, McCulloch, Rory, Mcdermott, Christopher, Mcdonald, Kerian, McGinniss, Laura, McGurk, Fiona, McIlwain, Jessica, McIver, Kirsten, Mckay, Pam, McKenna, Lorraine, Mclornan, Donal, McMahon, Coalon, McNeice, Linda, McNeill, Susan, McNickle, Molly, McQueen, Fiona, McRae, Simon, McTaggart, Bobby, Mehew, Jenny, Mehra, Varnn, Melly, Michelle, Menichelli, Tara, Micklethwatte, Ken, Mihailescue, Loredana, Mijovic, Aleksander, Millband, Hannah, Miller, Lucy, Millien, Samuel T., Milnthorpe, James, Minson, Adrian, Molnar, Eva, Monsour, Marc, Moody, Mary, Moon, Rebecca, Moore, Sally, Moore, Katy, Morgan, Kelly, Morralley, Rebecca, Morris, Denise, Morris, Kirk, Morrison, Nicole, Moss, Merinda, Mughal, Muhammad, Muir, Paul, Mukkath, David, Mulla, Aasiyu, Mulligan, Stephen, Mullings, Joanne, Mulqueen, Angela, Muluey, Caitlin, Murdoch, Sarah, Murrani, Sura, Murthy, Vidhya, Musngi, Jimmy, Mustafa, Nadreen, Mynes, Tracey, Nalpantidis, Anastasios, Nandurkar, Harshal, Nardone, Linda, Nasari, Latifa, Nash, Monica, Naylor, Georgina, Ngu, Loretta, Nguethina, Melissa, Nguyen, John, Nguyen, Joseph, Nichol, Wendy, Nicholls, Emma, Nicole, Catherine S., Nicolson, Phillip, Nielson, David, Nikolousis, Emmanouil, Nix, Georgina, Njoku, Rita, Norman, Jane, Norman, Amy, Norris, Phoebe, North, Daniel, Norwood, Megan, Notcheva, Gaynor, Novitzky-Basso, Igor, Nyaboko, Joseph, Nygren, Maria, Obu, Ingrid, O'Connell, Siobhan, O'Connor, Jody, O'Kelly, Deanna, O'Niell, Aideen, Ony, Jeremy, Oo, Kathy, Oo, April, Oppermann, Anne, Orr, Ruth, O'Sullivan, Mary, Page, Jennifer, Palfreyman, Emma, Paneesha, Shankaranarayana, Panicker, Shyam, Parbutt, Catherine, Parigi, Elesha, Paris, Gemma, Parker, Tracey, Parnell, Caroline, Parrish, Christopher, Parsons, Alex, Pasat, Mioara, Patel, Natasha, Patel, Vijay, Patel, Pooja, Patel, Chaya, Pati, Nalini, Patterson, Andrea, Paul, Lauren, Payet, Danielle, Payne, Elspeth, Peachey, Victoria, Pearson, Amanda, Peniket, Andy, Percy, Laura, Pereyra, Millicent, Pervaiz, Omer, Phalod, Gunjan D, Pham, Anh, Pho, Jason, Pickard, Keir, Pidcock, Michael, Piggin, Anna, Pishyar, Yalda, Pocock, Abigail, Pol, Ranjendres, Polzella, Paolo, Poolan, Sonia, Portingale, Vicki, Posnett, Claire, Potluri, Sandeep, Potter, Victoria, Pratt, Guy, Prodger, Catherine, Pueblo, Andres, Puliyayil, Anish, Puvanakumar, Pratheepan, Qadri, Abdul, Quach, Hang, Quinn, Michael, Rafferty, Mark, Rahman, Marzia, Raj, Kavita, Raj, Sonia, Rajendran, Ramina, Ramanan, Radha, Ramasamy, Karthik, Rampotas, Alexandros, Ranchhod, Natasha, Rashid, Sabia, Ratanjee, Sunita, Rathore, Gurpreet, Ratnasingam, Sumita, Rayat, Manjit, Rayner, Michael, Reddell-Denton, Rebecca, Redding, Nicola, Reddy, Udaya, Rehman, Atique, Rice, Carol, Riches, Iwona, Rider, Thomas, Riley, John, Rinaldi, Ciro, Roberts, Kayleigh, Roberts, Andrew, Robertson, Bryony, Robertson, Peter, Robinson, Dan, Robinson, Rebecca, Robjohns, Emma, Robledo, Laura, Rodrigues, Ana, Rofe, Chris, Roff, Bridie, Rogers, Rachel, Rolt, Jill, Rooney, Carmela, Rose, Kathy, Rose, Hannah, Ross, David, Rouf, Shahara, Rourke, Claire, Routledge, David, Ruggiero, Janet, Rule, Simon, Rumsey, Richard, Sagge, Cherry, Saldhana, Helen, Salisbury, Richard, Salisbury, Sarah, Salvaris, Ross, Sanders, Kay, Sangombe, Mirriam, Sanigorska, Anna, Santos, Kristine, Sarkis, Taylah, Sarma, Anita, Saunders, Natalie, Schmidt, Kara, Schmidtmann, Anja, Schumacher, Ann, Scorer, Tom Scorer, Scott, Asleigh, Seath, Ingrid, Sejman, Frances, Selim, Adrian, Shamim, Nadia, Shan, Jocelyn, Shanmuganathan, Naranie, Shanmugaranjan, Shaminie, Sharpe, Michelle, Sharpley, Faye, Shaw, Emma, Sheath, Cara, Sheehy, Oonagh, Shen, Vivian, Sherbide, Solomon, Sheridan, Mathew, Sheridan, Jane, Sheridon, Matthew, Shields, Tracy, Sim, Hau V, Sim, Shirlene, Sims, Matt, Singaraveloo, Lydia, Singh, Gurcharan, Singh, Jasmine, Sladesal, Shree, Sloan, Andrew, Slobodian, Peter, Smith, Sophie, Smith, Sarit, Smith, Claire, Smith, Alastair, Smith, Neil, Snowden, Katherine, Solis, Joel, Somios, Denise, Soo, Jade, Spanevello, Michelle, Spaulding, Madeleie, Spence, Laura, Spillane, Liz, Spiteri, Alisha, Sprigg, Naomi, Springett, Sally, Stafford, Lynn, Stainthorp, Katherine, Stark, Kate, Steeden, Louise, Stephen, Ella, Stephenson, Aisling, Stewart, Andrew, Stewart, Orla, Stobie, Emma, Stokes, Chelsea, Streater, Jacqui, Suddens, Charlie-Marie, Suntharalingam, Surenthini, Surana, Narinder, Sutherland, Robyn, Sutherland, Antony, Sutton, David, Sweeney, Connor, Sweet, Reilly, Szucs, Aniko P, Taheri, Leila E., Tailor, Hinesh, Tam, Constantine, Tambakis, George, Tamplin, Mary, Tan, Chee, Tan, Sui, Tan, Joanne, Tan, Zhi, Taran, Tatiana, Tarpey, Fiona, Taseka, Angela, Tasker, Suzy, Tatarczuch, Maciej, Tayabali, Sarrah, Taylor, Hannah, Taylor, Robert, Taylor, Melaine, Taylor-Moore, Ella, Teasdale, Lesley, Tebbet, Elizabeth, Tedjasepstra, Aditya, Tedjaseputra, Aditya, Tepkumkun, Oummy, Terpstra, Andrew, Thomas, Wayne, Thomas, Shanice, Thompson, Rachel, Thornton, Thomas, Thorp, Bronwyn, Thrift, Moi Yap, Thwaites, Phillipa, Timbres, Jasmine, Tindall, Lauren, Tiong, Ing Soo, Tippler, Nicole, Todd, Tony, Todd, Shirley, Toghill, Neil, Tomlinson, Eve, Tooth, Jacinta, Topp, M., Trail, Nicola, Tran, Nguyen, Tran, Elizabeth, Tran, Vi, Treder, Bona, Tribbeck, Michelle, Trochowski, Siobhan, Truslove, Maria, Tse, Tsun, Tseu, Bing, Tucker, David, Turner, Kelly, Turner, Dianne, Turner, Herleen, Turner, Gillian, Twohig, Julie, Tylee, Thomas, Uhe, Micheleine, Underhill, Lauren, V, Joanne, Van der Vliet, Georgina, Van Tonder, Tina, VanderWeyden, Carrie, Varghese, Jerry, Vaughan, Lachlan, Veale, David, Vickaryyous, Nicky, Vince, Kathryn, Von Welligh, Jacoba, Vora, Sona, Wadehra, Karan, Walker, Rebecca, Walker, Stephen, Wallace, Roslyn, Wallniosve, Stephanie, Wallwork, S., Walmsley, Zoe, Walters, Fiona, Wang, Joyce, Wang, Angela, Wang, Chen, Wanyika, Mercy, Warcel, Dana, Wardrobe, Katrina, Warnes, Kristian, Waterhouse, Christopher, Waterworth, Adam, Watson, Caroline, Watson, Edmund, Watts, Emily, Weaver, Emma, Weber, Nicholas, Webley, Kaytie, Welford, Anna, Wells, Matt, Westbury, Sarah, Westcott, Jackie, Western, Robyn, Weston, Julia, White, Jessica, White, Phillipa, Whitehead, Anna, Whitehouse, James, Wieringa, Samantha, Willan, John, Williams, Sandra, Williams, Bethany, Williamson, Stephanie, Willoughby, Brett, Wilmot, Gail, Wilmott, Rosalind, Wilson, Joanna, Wilson, Emma, Wilson, Suzy, Wilson, Heather, Wilson, Caroline, Wilson, Tanya, Wilton, Margaret, Wiltshire, Paula, Wincup, Joanne, Wolf, Julia, Wong, Henna, Wong, Cyndi, Wong, Daniel, Wong, Jonathan, Wong, Shi Qin, Wood, Sarah, Wood, Henry, Wooding, Jackie, Woolley, Kelly, Wright, Myles, Wynn-Williams, Roland, Yannakou, Costas, Yeoh, Zhi Han, Yeung, David, Young, Agnes, Yuen, Flora, Yuen, Agnes, Zaja, Oliver, Zhang, Xiao-Yin, and Zhang, Mei

Published
2025
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9. Introduction. Nineteenth-Century Protest Movements in Britain: A Survey of Historians' Approaches.

Author

Bensimon, Fabrice and Rogers, Rachel

Abstract

Copyright of French Journal of British Studies / Revue Française de Civilisation Britannique is the property of Centre de Recherches et d'Etudes en Civilisation Britannique and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2025

14. Erratum: Current Breast Milk PFAS Levels in the United States and Canada: After All This Time, Why Don't We Know More?

Author

LaKind, Judy S., Verner, Marc-Andre, Rogers, Rachel D., Goeden, Helen, Naiman, Daniel Q., Marchitti, Satori A., Lehmann, Geniece M., Hines, Erin P., and Fenton, Suzanne E.

Abstract

During the paper editing process, two values in Table 2 were mistakenly modified. Milk:serum concentration ratios from the 'Karrman et al. 2007' study should be 0.01 for PFOS and 0.01 [...]

Published
2023
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15. MANAGING THE DISRUPTION OF UNIVERSITY MERGERS: INSIGHTS FROM A HEALTHCARE ADMINISTRATION PROGRAM.

Author

MCINTYRE, REBECCA, JOHNSON, SALLIE BETH, and ROGERS, RACHEL L.

Abstract

Highly disruptive university mergers, alliances, and acquisitions (MA&A) are on the rise in higher education. There are several program management issues for healthcare administration academic programs to anticipate and overcome to successfully navigate MA&A. This case report presents firsthand experiences from Bachelor of Science in Healthcare Administration (BSHA) and Master of Healthcare Administration (MHA) programs involved in a privatepublic university merger (2019--2024). Overall disruptions involved in their university MA&A included programmatic issues related to academic and student affairs, enrollment management, strategic communications, finance, and administration. Insights and recommendations for managing academic reorganization, policies and procedures, communications, marketing, blending IT infrastructure, and program delivery during MA&A are offered. [ABSTRACT FROM AUTHOR]

Published
2024

21. Bayesian Estimation of Human Population Toxicokinetics of PFOA, PFOS, PFHxS, and PFNA from Studies of Contaminated Drinking Water

Author

Chiu, Weihsueh A., Lynch, Meghan T., Lay, Claire R., Antezana, Adriana, Malek, Parker, Sokolinski, Sara, and Rogers, Rachel D.

Subjects
Testing, Contamination, Methods, Health aspects, Bayesian analysis -- Methods, Perfluoroalkyl and polyfluoroalkyl substances (PFAS) -- Testing -- Health aspects, Drinking water -- Contamination, Bayesian statistical decision theory -- Methods
Abstract

Introduction Poly- and perfluoroalkyl substances (PFAS) are man-made chemicals consisting of chains of linked carbon and fluorine atoms. Their structure gives them unique physicochemical properties that have been found useful [...], Background: Setting health-protective standards for poly- and perfluoroalkyl substances (PFAS) exposure requires estimates of their population toxicokinetics, but existing studies have reported widely varying PFAS half-lives ([T.sub.1/2]) and volumes of distribution ([V.sub.d]). Objectives: We combined data from multiple studies to develop harmonized estimates of [T.sub.1/2] and [V.sub.d], along with their interindividual variability, for four PFAS commonly found in drinking water: perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoic acid (PFNA), and perfluorohexane sulfonate (PFHxS). Methods: We identified published data on PFAS concentrations in human serum with corresponding drinking water measurements, separated into training and testing data sets. We fit training data sets to a one-compartment model incorporating interindividual variability, time-dependent drinking water concentrations, and background exposures. Use of a hierarchical Bayesian approach allowed us to incorporate informative priors at the population level, as well as at the study level. We compared posterior predictions to testing data sets to evaluate model performance. Results: Posterior median (95% CI) estimates of [T.sub.1/2] (in years) for the population geometric mean were 3.14 (2.69, 3.73) for PFOA, 3.36 (2.52, 4.42) for PFOS, 2.35 (1.65, 3.16) for PFNA, and 8.30 (5.38, 13.5) for PFHxS, all of which were within the range of previously published values. The extensive individual-level data for PFOA allowed accurate estimation of population variability, with a population geometric standard deviation of 1.57 (95% CI: 1.42, 1.73); data from other PFAS were also consistent with this degree of population variability. [V.sub.d] estimates ranged from 0.19 to 0.43 L/kg across the four PFAS, which tended to be slightly higher than previously published estimates. Discussion: These results have direct application in both risk assessment (quantitative interspecies extrapolation and uncertainty factors for interindividual variability) and risk communication (interpretation of monitoring data). In addition, this study provides a rigorous methodology for further refinement with additional data, as well as application to other PFAS. https://doi.org/10.1289/EHP10103

Published
2022
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22. Gut-educated IgA plasma cells defend the meningeal venous sinuses

Author

Fitzpatrick, Zachary, Frazer, Gordon, Ferro, Ashley, Clare, Simon, Bouladoux, Nicolas, Ferdinand, John, Tuong, Zewen Kelvin, Negro-Demontel, Maria Luciana, Kumar, Nitin, Suchanek, Ondrej, Tajsic, Tamara, Harcourt, Katherine, Scott, Kirsten, Bashford-Rogers, Rachel, Helmy, Adel, Reich, Daniel S., Belkaid, Yasmine, Lawley, Trevor D., McGavern, Dorian B., and Clatworthy, Menna R.

Published
2020
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25. Results from the randomized phase III DREAMM-7 study of belantamab mafodotin (belamaf) + bortezomib, and dexamethasone (BVd) vs daratumumab, bortezomib, and dexamethasone (DVd) in relapsed/refractory multiple myeloma (RRMM).

Author

Mateos, Maria-Victoria, primary, Robak, Pawel, additional, Hus, Marek, additional, Xia, Zhongjun, additional, Zherebtsova, Vera, additional, Ward, Christopher, additional, Ho, P. Joy, additional, Hajek, Roman, additional, Kim, Kihyun, additional, Dimopoulos, Meletios A., additional, Cerchione, Claudio, additional, Riccio, Antonio, additional, McKeown, Astrid, additional, Rogers, Rachel, additional, Baig, Hena, additional, Eccersley, Lydia, additional, Roy-Ghanta, Sumita, additional, Opalinska, Joanna, additional, and Hungria, Vania, additional

Published
2024
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27. Non-physical components of perceived facial attractiveness

Author

Rogers, Rachel

Subjects
302.13
Abstract

The idea that a not-so-attractive person, is perceived as increasingly attractive the more one gets to know them, is a commonly reported experience, which is often attributed to familiarity. Features initially judged as unattractive, may later be judged as cute or unique. However, this explanation cannot account for why a person may be perceived as less attractive as one gets to know them. The first aim of the present research was to investigate whether liking for the target is the critical mediating factor in the relationship between knowing a person's non-physical characteristics, and appraisals of their facial attractiveness. This hypothesis is confirmed in Chapter 2. The second aim of this research was to explore the social-cognitive mechanisms underlying this effect. Chapter 2 found that personality infonnation, with a strong association to liking, cues expectations regarding the physical attractiveness of the target, which predict actual attractiveness ratings. Chapter 3 examined whether the effects are specific to the faces to which the personality information referred, and found that the effect carried-over to influence evaluations of a subsequently presented novel face, but that the effect was not mediated by whether the novel face physically resembled the target face, or was a friend of the target person. Chapter 4 found that affective priming, with words unrelated to personality characteristics, affects evaluations of facial attractiveness in much the same way. However, nonaffective priming does not influence ratings of attractiveness and relevant facial characteristics in the same way as affective priming does. Chapter 5 found that negative stimuli had a larger impact on affect scores and attractiveness judgements than positive stimuli. Chapter 6 presents a social-cognitive model of the effect of non-physical factors which influence how a face is processed to fonn attractiveness judgements, which brings together the different findings of the present research.

Published
2012

29. Tissue-resident NK cells support survival in pancreatic cancer through promotion of cDC1-CD8T activity

Author

Go, Simei, primary, Demetriou, Constantinos, additional, Hughes, Sophie, additional, Lanfredini, Simone, additional, Valenzano, Giampeiro, additional, Ferry, Helen, additional, Arbe-Barnes, Edward, additional, Sivakumar, Shivan, additional, Bashford-Rogers, Rachel, additional, Middleton, Mark R, additional, Mukherjee, Somnath, additional, Morton, Jennifer, additional, Jones, Keaton, additional, and O'Neill, Eric, additional

Published
2023
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30. Demonstrative Evidence and the Use of Algorithms in Jury Trials.

Author

ROGERS, RACHEL and VANDERPLAS, SUSAN

Subjects
LEGAL evidence, JURY trials, JURY, EXPERT evidence, SCIENTIFIC errors
Abstract

We investigate how the use of bullet comparison algorithms and demonstrative evidence may affect juror perceptions of reliability, credibility, and understanding of expert witnesses and presented evidence. The use of statistical methods in forensic science is motivated by a lack of scientific validity and error rate issues present in many forensic analysis methods. We explore what our study says about how this type of forensic evidence is perceived in the courtroom - where individuals unfamiliar with advanced statistical methods are asked to evaluate results in order to assess guilt. In the course of our initial study, we found that individuals overwhelmingly provided high Likert scale ratings in reliability, credibility, and scientificity regardless of experimental condition. This discovery of scale compression - where responses are limited to a few values on a larger scale, despite experimental manipulations - limits statistical modeling but provides opportunities for new experimental manipulations which may improve future studies in this area. [ABSTRACT FROM AUTHOR]

Published
2024
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39. The Cato Street Conspiracy: Plotting, Counter-Intelligence and the Revolutionary Tradition in Britain and Ireland, ed. Jason McElligott and Martin Conboy, Manchester: Manchester University Press, 2020, 197 p

Author

Rogers, Rachel

Subjects
espionage, Six Acts, Napoleonic Wars, réforme radicale, Society of Spencean Philanthropists, massacre de Peterloo, rébellion irlandaise, Cato Street conspiracy, espionnage, guerres napoléoniennes, Irish Rebellion, Peterloo massacre, revolutionary tradition, tradition révolutionnaire, conspiration de Cato Street, ultra-radicalism
Abstract

On 23rd February 1820, a plot concocted by a group of London ultra-radicals to kill members of the Tory cabinet was foiled, thanks to a combination of internal disunity, ministerial alertness, and the timely intervention of an agent provocateur. Reports by informants, whose precise role and dealings with ministers is meticulously traced by Richard A. Gaunt in the opening chapter of this collection, led to a raid by Bow Street Runners on a hay loft in Cato Street. It was at this location that ...

Published
2023

40. Afterword. 'An unjust attack on a peaceful demonstration that had the legal and moral right to meet'. In correspondence on Peterloo with Katrina Navickas

Author

Rogers, Rachel

Subjects
General Medicine
Abstract

RR: You wrote in the London Review of Books in 2018 that “Peterloo is the story of a moment of terrible repression by the few of the many, and we shouldn’t be afraid of saying that the working-class demonstrators were in the right.” Could you comment further on this view in your capacity as a historian of British popular protest in the eighteenth and nineteenth centuries? KN: My statement about the Peterloo Massacre relates to the way that the event and its significance has been portrayed in ...

Published
2021
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43. Utility of the Counseling Center Assessment of Psychological Symptoms Screen in a Collegiate Athlete Population.

Author

Higgins, Kate, Woods, Brett, Haskell, Brett, Bullock, Mariah, Rogers, Rachel, and Izuegbunam, Nedu

Subjects
COMPETENCY assessment (Law), POPULATION, DECISION trees, COUNSELING, RESEARCH methodology evaluation, MEDICAL screening, CASE-control method, ACQUISITION of data, PSYCHOLOGICAL tests, ATTENTION-deficit hyperactivity disorder, DESCRIPTIVE statistics, MEDICAL records, CHI-squared test, PSYCHOLOGICAL adaptation, LOGISTIC regression analysis, STATISTICAL sampling, DATA analysis software, SOCIODEMOGRAPHIC factors, ODDS ratio, PSYCHOLOGICAL distress, MENTAL health services
Abstract

Mental health screening as a part of collegiate athletic preparticipation evaluations is becoming increasingly common, but effective and efficient screening depends on a screening tool that can accurately identify mental health symptoms and the need for mental health intervention. Case-control study. Archival clinical records review. Two cohorts of incoming National Collegiate Athletics Association Division I collegiate athletes (N = 353). Athletes completed the Counseling Center Assessment of Psychological Symptoms (CCAPS) Screen as a part of their preparticipation evaluation. These data were then matched with basic demographic data and mental health treatment history from clinical records, and the utility of the CCAPS Screen in determining a future or ongoing need for mental health services was analyzed. Score differences for each of the 8 CCAPS Screen scales (Depression, Generalized Anxiety, Social Anxiety, Academic Distress, Eating Concerns, Frustration, Family Distress, and Alcohol Use) were found based on several demographic variables. Logistic regression analysis demonstrated that female sex, team sport participation, and the Generalized Anxiety scale score predicted future participation in mental health treatment. Decision tree testing of the CCAPS scales showed low utility in classifying those who received mental health treatment versus those who did not. The CCAPS Screen did not appear to differentiate well between those who eventually received mental health services and those who did not. This should not be taken to mean that mental health screening is not useful but rather that a 1-time, state-based screening is not sufficient for athletes who experience intermittent but recurring stressors in a dynamic environment. A proposed model for improving the current standard of practice for mental health screening is provided as a focus of future research. [ABSTRACT FROM AUTHOR]

Published
2023
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45. Poster: MM-459 Safety and Clinical Activity of Belantamab Mafodotin With Lenalidomide Plus Dexamethasone in Patients With Relapsed/Refractory Multiple Myeloma (RRMM): DREAMM-6 Arm-A Interim Analysis

Author

Quach, Hang, primary, Gironella, Mercedes, additional, Lee, Cindy, additional, Popat, Rakesh, additional, Cannell, Paul, additional, Kasinathan, Ravi S., additional, Chopra, Bikramjit, additional, Rogers, Rachel, additional, Ferron-Brady, Geraldine, additional, Shafi-Harji, Seema, additional, Patel, Nashita, additional, Opalinska, Joanna, additional, Gupta, Ira, additional, and Augustson, Bradley, additional

Published
2022
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46. Oral Abstract: MM-459 Safety and Clinical Activity of Belantamab Mafodotin with Lenalidomide Plus Dexamethasone in Patients with Relapsed/Refractory Multiple Myeloma (RRMM): DREAMM-6 Arm-A Interim Analysis

Author

Quach, Hang, primary, Gironella, Mercedes, additional, Lee, Cindy, additional, Popat, Rakesh, additional, Cannell, Paul, additional, Kasinathan, Ravi S., additional, Chopra, Bikramjit, additional, Rogers, Rachel, additional, Ferron-Brady, Geraldine, additional, Shafi-Harji, Seema, additional, Patel, Nashita, additional, Opalinska, Joanna, additional, Gupta, Ira, additional, and Augustson, Bradley, additional

Published
2022
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48. The Role of Personal and Contextual Variables in College Students' Academic Achievement

Author

Rogers, Rachel A.

Abstract

College matriculation rates are increasing but graduation rates are failing to parallel the increased enrollment. One reason for this discrepancy may be that many college students are unable to regulate their own learning. This dissertation examined the Self-Regulated Learning (SRL; Pintrich, 2004) model in students taking Statistics in Psychology and Research Methods. The inclusion of the constructs of possible selves and identity development in the SRL model was proposed, as was the Achievement Goal Questionnaire (AGQ; Elliot & McGregor, 2001), a measure of the 2x2 Framework of achievement goal orientation. These variables were assessed along with those included in the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich, Smith, Garcia & McKeachie, 1993). Results indicated that possible selves and the AGQ are not useful predictors of the academic outcomes of test grade and expected final grade. Ego identity status, however, was a significant predictor of course outcomes. The best single predictor was self-efficacy for learning from the MSLQ. Multiple regression models accounted for 27-36% of the variance in test grades and 49-67% of the variance in expected final grades. Evaluation of strategy change over the course of a semester revealed that students do adjust their study strategies and motivational beliefs effectively. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Published
2010

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