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53. HTH-14 A multi-centre analysis of AUGIB; urea-creatinine ratiois a useful predictor for bleeding, and endotherapy

Author

Serna, Solange, primary, Segal, Jonathan, additional, Abbasi, Abdullah, additional, Aleem, Junaid, additional, Alhamamy, Noor, additional, Alkoury, Jad, additional, Anjum, Raheel, additional, Disney, Mr Benjamin, additional, Howard, Emma, additional, Hundle, Aaron, additional, Hussain, Nasir, additional, Ismail, Mr Asem, additional, McFarlane, Michael, additional, Mozdiak, Ella, additional, Port, Saskia, additional, Rattehalli, Deepa, additional, Schembri, John, additional, Silva, Geeth, additional, Thoufeeq, Mo, additional, and Verma, Ajay, additional

Published
2021
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57. Students’ In-class and Out of Class Mathematical Activities

Author

Rasmussen, Chris, Fredriksen, Helge, Howard, Emma, Pepin, Birgit, Rämö, Johanna, Durand-Guerrier, Viviane, Hochmuth, Reinhard, Nardi, Elena, Winsløw, Carl, Department of Mathematics and Statistics, and Mathematics Education Research Group

Subjects
111 Mathematics, 516 Educational sciences
Abstract

In this chapter, the authors zoom in on students’ in-class and out-of-class practices, use of resources out-of-class, role in assessment practices, responses to active learning initiatives, and in-class mathematical practices. They do so with a sharp theoretical focus on situating students’ mathematical practices in relation to interactions with other students, the teacher, the mathematics, and resources. They conclude by considering how future research might engage in cross-pollination of the above themes and they highlight potential benefits from theoretical networking as well as from deploying theories that are appropriate for, but hitherto under-used in, examining students’ in-class and out-of-class practices in complementary ways.

Published
2021

62. The contribution of X-linked coding variation to severe developmental disorders

Author

Martin, Hilary C., Gardner, Eugene J., Samocha, Kaitlin E., Kaplanis, Joanna, Akawi, Nadia, Sifrim, Alejandro, Eberhardt, Ruth Y., Tavares, Ana Lisa Taylor, Neville, Matthew D. C., Niemi, Mari E. K., Gallone, Giuseppe, McRae, Jeremy, Wright, Caroline F., FitzPatrick, David R., Firth, Helen V., Hurles, Matthew E., Borras, Silvia, Clark, Caroline, Dean, John, Miedzybrodzka, Zosia, Ross, Alison, Tennant, Stephen, Dabir, Tabib, Donnelly, Deirdre, Humphreys, Mervyn, Magee, Alex, McConnell, Vivienne, McKee, Shane, McNerlan, Susan, Morrison, Patrick J., Rea, Gillian, Stewart, Fiona, Cole, Trevor, Cooper, Nicola, Cooper-Charles, Lisa, Cox, Helen, Islam, Lily, Jarvis, Joanna, Keelagher, Rebecca, Lim, Derek, McMullan, Dominic, Morton, Jenny, Naik, Swati, O’Driscoll, Mary, Ong, Kai-Ren, Osio, Deborah, Ragge, Nicola, Turton, Sarah, Vogt, Julie, Williams, Denise, Bodek, Simon, Donaldson, Alan, Hills, Alison, Low, Karen, Newbury-Ecob, Ruth, Norman, Andrew M., Roberts, Eileen, Scurr, Ingrid, Smithson, Sarah, Tooley, Madeleine, Abbs, Steve, Armstrong, Ruth, Dunn, Carolyn, Holden, Simon, Park, Soo-Mi, Paterson, Joan, Raymond, Lucy, Reid, Evan, Sandford, Richard, Simonic, Ingrid, Tischkowitz, Marc, Woods, Geoff, Bradley, Lisa, Comerford, Joanne, Green, Andrew, Lynch, Sally, McQuaid, Shirley, Mullaney, Brendan, Berg, Jonathan, Goudie, David, Mavrak, Eleni, McLean, Joanne, McWilliam, Catherine, Reavey, Eleanor, Azam, Tara, Cleary, Elaine, Jackson, Andrew, Lam, Wayne, Lampe, Anne, Moore, David, Porteous, Mary, Baple, Emma, Baptista, Júlia, Brewer, Carole, Castle, Bruce, Kivuva, Emma, Owens, Martina, Rankin, Julia, Shaw-Smith, Charles, Turner, Claire, Turnpenny, Peter, Tysoe, Carolyn, Bradley, Therese, Davidson, Rosemarie, Gardiner, Carol, Joss, Shelagh, Kinning, Esther, Longman, Cheryl, McGowan, Ruth, Murday, Victoria, Pilz, Daniela, Tobias, Edward, Whiteford, Margo, Williams, Nicola, Barnicoat, Angela, Clement, Emma, Faravelli, Francesca, Hurst, Jane, Jenkins, Lucy, Jones, Wendy, Kumar, V.K.Ajith, Lees, Melissa, Loughlin, Sam, Male, Alison, Morrogh, Deborah, Rosser, Elisabeth, Scott, Richard, Wilson, Louise, Beleza, Ana, Deshpande, Charu, Flinter, Frances, Holder, Muriel, Irving, Melita, Izatt, Louise, Josifova, Dragana, Mohammed, Shehla, Molenda, Aneta, Robert, Leema, Roworth, Wendy, Ruddy, Deborah, Ryten, Mina, Yau, Shu, Bennett, Christopher, Blyth, Moira, Campbell, Jennifer, Coates, Andrea, Dobbie, Angus, Hewitt, Sarah, Hobson, Emma, Jackson, Eilidh, Jewell, Rosalyn, Kraus, Alison, Prescott, Katrina, Sheridan, Eamonn, Thomson, Jenny, Bradshaw, Kirsty, Dixit, Abhijit, Eason, Jacqueline, Haines, Rebecca, Harrison, Rachel, Mutch, Stacey, Sarkar, Ajoy, Searle, Claire, Shannon, Nora, Sharif, Abid, Suri, Mohnish, Vasudevan, Pradeep, Canham, Natalie, Ellis, Ian, Greenhalgh, Lynn, Howard, Emma, Stinton, Victoria, Swale, Andrew, Weber, Astrid, Banka, Siddharth, Breen, Catherine, Briggs, Tracy, Burkitt-Wright, Emma, Chandler, Kate, Clayton-Smith, Jill, Donnai, Dian, Douzgou, Sofia, Gaunt, Lorraine, Jones, Elizabeth, Kerr, Bronwyn, Langley, Claire, Metcalfe, Kay, Smith, Audrey, Wright, Ronnie, Bourn, David, Burn, John, Fisher, Richard, Hellens, Steve, Henderson, Alex, Montgomery, Tara, Splitt, Miranda, Straub, Volker, Wright, Michael, Zwolinski, Simon, Allen, Zoe, Bernhard, Birgitta, Brady, Angela, Brooks, Claire, Busby, Louise, Clowes, Virginia, Ghali, Neeti, Holder, Susan, Ibitoye, Rita, Wakeling, Emma, Blair, Edward, Carmichael, Jenny, Cilliers, Deirdre, Clasper, Susan, Gibbons, Richard, Kini, Usha, Lester, Tracy, Nemeth, Andrea, Poulton, Joanna, Price, Sue, Shears, Debbie, Stewart, Helen, Wilkie, Andrew, Albaba, Shadi, Baker, Duncan, Balasubramanian, Meena, Johnson, Diana, Parker, Michael, Quarrell, Oliver, Stewart, Alison, Willoughby, Josh, Crosby, Charlene, Elmslie, Frances, Homfray, Tessa, Jin, Huilin, Lahiri, Nayana, Mansour, Sahar, Marks, Karen, McEntagart, Meriel, Saggar, Anand, Tatton-Brown, Kate, Butler, Rachel, Clarke, Angus, Corrin, Sian, Fry, Andrew, Kamath, Arveen, McCann, Emma, Mugalaasi, Hood, Pottinger, Caroline, Procter, Annie, Sampson, Julian, Sansbury, Francis, Varghese, Vinod, Baralle, Diana, Callaway, Alison, Cassidy, Emma J., Daniels, Stacey, Douglas, Andrew, Foulds, Nicola, Hunt, David, Kharbanda, Mira, Lachlan, Katherine, Mercer, Catherine, Side, Lucy, Temple, I. Karen, Wellesley, Diana, Martin, Hilary C. [0000-0002-4454-9084], Gardner, Eugene J. [0000-0001-9671-1533], Samocha, Kaitlin E. [0000-0002-1704-3352], Eberhardt, Ruth Y. [0000-0001-6152-1369], Tavares, Ana Lisa Taylor [0000-0001-7089-0502], Neville, Matthew D. C. [0000-0001-5816-7936], Niemi, Mari E. K. [0000-0003-0696-6175], Wright, Caroline F. [0000-0003-2958-5076], Hurles, Matthew E. [0000-0002-2333-7015], and Apollo - University of Cambridge Repository

Subjects
631/208/366, 49/23, article, 631/208/1516, 631/208/205
Abstract

Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders.

Published
2021
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63. Improving service use through prediction modelling: a case study of a mathematics support centre.

Author

Howard, Emma and Cronin, Anthony

Subjects
PREDICTION models, MATHEMATICS, BUSINESS hours, SYSTEMS software, VALUE (Economics), INTELLIGENT tutoring systems
Abstract

In higher education, student learning support centres are examples of walk-in services with nonstationary demand. For many centres, the major expenditure is tutor wages; thus, optimizing tutor numbers and ensuring value for money in this area are key. In University College Dublin, the mathematics support centre (MSC) has developed a software system, which electronically records the time each student enters the queue, their start time with a tutor and time spent with a tutor. In this paper, we show how data analysis of 25,702 student visits and tutor timetable data, spanning 6 years, is used to identify busy and quiet periods. Prediction modelling is then used to estimate the waiting time for future MSC visitors. Subsequently, we discuss how this is used for staffing optimization, i.e. to ensure there is sufficient coverage for busy times and no resource wastage during quieter periods. The analysis described resulted in the MSC reducing the number of queue abandonments and releasing funds from overstaffed hours to increase opening hours. The methods used are easily adapted for any busy walk-in service, and the code and data referenced are freely available: https://github.com/ehoward1/Math-Support-Centre-. [ABSTRACT FROM AUTHOR]

Published
2022
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68. O68 Implementation of systematic lynch syndrome testing in colorectal cancer: outcomes from a pilot pathway

Author

Collins, Paul, primary, Andrews, Tim, additional, Atkinson, Emma, additional, Davis, Kerrie, additional, Greenhalgh, Lynn, additional, Howard, Emma, additional, Lavelle, Anna, additional, Lofthouse, Matt, additional, McNicol, Fran, additional, O’Connor, Joanne, additional, Quinn, Rachel, additional, Reid, Alistair, additional, Skaife, Paul, additional, and Campbell, Fiona, additional

Published
2021
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69. The effectiveness of debt relief in mitigating the macroeconomic consequences of natural disasters

Author

Howard, Emma, Murphy, Kean, Nientker, Wouter, El-Ouaghlidi, Karim, and Schmidt, Harry

Subjects
Official development assistance, Deutes – Alleujament, Debt Relief, Natural disasters, Catàstrofes naturals, Treball de fi de màster – Curs 2019-2020
Abstract

Treball fi de màster de: Master's Degree in Specialized Economic Analysis Directors: Fernando Broner ; Manuel García-Santana We employ a dynamic panel fixed effects model to assess the macroeconomic consequences of natural disasters in Africa and the effectiveness of debt relief in promoting post-disaster recovery. Our results show that floods and droughts have a negative contemporaneous effect on GDP growth which, in the case of floods, is recovered at the second lag. General debt relief - consisting of debt forgiveness, rescheduling, and significant increases in ODA - has no impact on economic growth. However, significant increases in ODA in the aftermath of disasters have a positive effect that is increasing in disaster severity, suggesting that it is an effective post-disaster recovery instrument. Debt-to-GDP growth and crop production growth are also considered in the analysis. Las implicancias de los desastres naturales en África y el consecuente alivio de la deuda soberana son los objetos de estudio de este trabajo. Empleamos un modelo de efectos fijos de panel dinámico para evaluar las consecuencias macroeconómicas de los desastres y la eficacia del alivio de la deuda para facilitar la recuperación posterior a los mismos. El alivio general de la deuda - que consiste en la condonación de la deuda, el reescalonamiento y aumentos significativos de la AOD - no tiene repercusiones en el crecimiento económico. Sin embargo, los aumentos significativos de la AOD después de los desastres tienen un efecto positivo que aumenta de acuerdo a la gravedad de los desastres. Esto sugiere que es un instrumento eficaz de recuperación después de los desastres.

Published
2020

70. Additional file 4 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 4. Graphs 1 to 210 (pages 1 to 35).

Published
2020
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71. Additional file 7 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 7. Graphs 631 to 840 (pages 106 to 140).

Published
2020
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72. Additional file 6 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 6. Graphs 421 to 630 (pages 71 to 105).

Published
2020
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73. Additional file 8 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 8. Graphs 841 to 1050 (pages 141 to 175).

Published
2020
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74. Additional file 1 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 1. Figures S1 to S10, and additional text on the causal ordering of variables.

Published
2020
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75. Additional file 5 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 5. Graphs 211 to 420 (pages 36 to 70).

Published
2020
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76. Additional file 3 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 3. Index and notes on supplementary graphs 1 to 205 (page 1 to 205).

Published
2020
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77. Effect of a Primary Care Walking Intervention with and without Nurse Support on Physical Activity Levels in 45- to 75-Year-Olds: The Pedometer And Consultation Evaluation (PACE-UP) Cluster Randomised Clinical Trial

Author

Harris, Tess, Kerry, Sally M., Limb, Elizabeth S., Victor, Christina R., Iliffe, Steve, Ussher, Michael, Whincup, Peter H., Ekelund, Ulf, Fox-Rushby, Julia, Furness, Cheryl, Anokye, Nana, Ibison, Judith, DeWilde, Steve, David, Lee, Howard, Emma, Dale, Rebecca, Smith, Jaime, and Cook, Derek G.

Subjects
Pedometers -- Usage, Physically disabled persons -- Physiological aspects, Primary health care -- Methods, Biological sciences
Abstract

Background Pedometers can increase walking and moderate-to-vigorous physical activity (MVPA) levels, but their effectiveness with or without support has not been rigorously evaluated. We assessed the effectiveness of a pedometer-based walking intervention in predominantly inactive adults, delivered by post or through primary care nurse-supported physical activity (PA) consultations. Methods and Findings A parallel three-arm cluster randomised trial was randomised by household, with 12-mo follow-up, in seven London, United Kingdom, primary care practices. Eleven thousand fifteen randomly selected patients aged 45-75 y without PA contraindications were invited. Five hundred forty-eight self-reporting achieving PA guidelines were excluded. One thousand twenty-three people from 922 households were randomised between 2012-2013 to one of the following groups: usual care (n = 338); postal pedometer intervention (n = 339); and nurse-supported pedometer intervention (n = 346). Of these, 956 participants (93%) provided outcome data (usual care n = 323, postal n = 312, nurse-supported n = 321). Both intervention groups received pedometers, 12-wk walking programmes, and PA diaries. The nurse group was offered three PA consultations. Primary and main secondary outcomes were changes from baseline to 12 mo in average daily step-counts and time in MVPA (in [greater than or equal to]10-min bouts), respectively, measured objectively by accelerometry. Only statisticians were masked to group. Analysis was by intention-to-treat. Average baseline daily step-count was 7,479 (standard deviation [s.d.] 2,671), and average time in MVPA bouts was 94 (s.d. 102) min/wk. At 12 mo, mean steps/d, with s.d. in parentheses, were as follows: control 7,246 (2,671); postal 8,010 (2,922); and nurse support 8,131 (3,228). PA increased in both intervention groups compared with the control group; additional steps/d were 642 for postal (95% CI 329-955) and 677 for nurse support (95% CI 365-989); additional MVPA in bouts (min/wk) were 33 for postal (95% CI 17-49) and 35 for nurse support (95% CI 19-51). There were no significant differences between the two interventions at 12 mo. The 10% (1,023/10,467) recruitment rate was a study limitation. Conclusions A primary care pedometer-based walking intervention in predominantly inactive 45- to 75-y-olds increased step-counts by about one-tenth and time in MVPA in bouts by about one-third. Nurse and postal delivery achieved similar 12-mo PA outcomes. A primary care pedometer intervention delivered by post or with minimal support could help address the public health physical inactivity challenge. Clinical Trial Registration isrctn.com ISRCTN98538934., Author(s): Tess Harris 1,*, Sally M. Kerry 2, Elizabeth S. Limb 1, Christina R. Victor 3, Steve Iliffe 4, Michael Ussher 1, Peter H. Whincup 1, Ulf Ekelund 5,6, Julia [...]

Published
2017
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80. Weather patterns in Southeast Asia: Relationship with tropical variability and heavy precipitation.

Author

Howard, Emma, Thomas, Simon, Frame, Thomas H. A., Gonzalez, Paula L. M., Methven, John, Martínez-Alvarado, Oscar, and Woolnough, Steven J.

Subjects
*PRECIPITATION variability, *WEATHER, *WEATHER forecasting, *MODES of variability (Climatology), *MADDEN-Julian oscillation, *TROPICAL cyclones, *MONSOONS
Abstract

Two sets of weather patterns describing variability in 850 hPa winds in Southeast Asia are presented and compared. Patterns are calculated using EOF/k-means clustering with and without imposing a separation between planetary-scale and regional-scale circulation features. The former are labelled as tiered patterns while the latter are referred to as flat. The ability of the patterns to distinguish between known modes of tropical circulation variability is examined. This includes climate modes such as the seasonal monsoons, the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) as well as sub-seasonal modes including cold surges, phases of the MJO and Boreal summer intraseasonal oscillation (BSISO), tropical cyclones, Borneo vortices and equatorial waves. All these modes are well captured by the weather patterns except for the equatorial waves and the IOD. The tiered patterns are shown to better describe large-scale modes of variability, while the flat patterns better describe the synoptic variability. Both sets of weather patterns are then used to study the likelihood of heavy precipitation depending on synoptic circulation by considering the regime-conditioned probability of high-percentile precipitation using the satellite-derived Global Precipitation Measurement (GPM) dataset. It is shown that the pattern centroids explain up to 10% of the seasonally anomalous precipitation over land, and that a perfect weather pattern forecast would outperform a perfect MJO forecast. These weather patterns show promising potential in extending the useful forecast range for the risk of heavy precipitation, dependent on their forecastability. [ABSTRACT FROM AUTHOR]

Published
2022
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83. Silicon photonics for artificial intelligence applications

Author

Marquez, Bicky A., primary, Filipovich, Matthew J., additional, Howard, Emma R., additional, Bangari, Viraj, additional, Guo, Zhimu, additional, Morison, Hugh D., additional, Ferreira De Lima, Thomas, additional, Tait, Alexander N., additional, Prucnal, Paul R., additional, and Shastri, Bhavin J., additional

Published
2020
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90. An exploration of the relationship between continuous assessment and resource use in a service mathematics module

Author

Howard, Emma, Meehan, Maria, Parnell, Andrew, School of Mathematics and Statistics, University College Dublin, Hamilton Institute, Maynooth University, Ireland, National University of Ireland Maynooth (Maynooth University), Insight Centre for Data Analytics, Utrecht University, Uffe Thomas Jankvist, Marja van den Heuvel-Panhuizen, Michiel Veldhuis, and Veldhuis, Michiel

Subjects
Continuous assessment, service mathematics, undergraduate mathematics, [SHS.EDU]Humanities and Social Sciences/Education, [SHS.EDU] Humanities and Social Sciences/Education, student participation, remediation, ComputingMilieux_COMPUTERSANDEDUCATION, [MATH] Mathematics [math], [MATH]Mathematics [math]
Abstract

International audience; Previous research has shown that students’ use of module resources strongly relates to the timing of the module’s continuous assessment. In our case study of a large first-year mathematics module for Business students, Maths for Business, we examine this relationship and the resources relied on by students for completing their continuous assessment. In Maths for Business, students have the choice of using live lectures or online videos or a combination of both. We find that students who incorporate lectures into their approach engage consistently throughout each week with module resources, while others adopt a just-in-time approach for each weekly quiz. We also show that the introduction of remediation of quizzes can boost participation with resources, in particular feedback resources.

Published
2019

93. A pedometer-based walking intervention in 45- to 75-year-olds, with and without practice nurse support: the PACE-UP three-arm cluster RCT

Author

Harris, Tess, Kerry, Sally, Victor, Christina, Iliffe, Steve, Ussher, Michael, Fox-Rushby, Julia, Whincup, Peter, Ekelund, Ulf, Furness, Cheryl, Limb, Elizabeth, Anokye, Nana, Ibison, Judith, DeWilde, Stephen, David, Lee, and Howard, Emma

Subjects
Health Policy
Abstract

Background Guidelines recommend walking to increase moderate to vigorous physical activity (MVPA) for health benefits. Objectives To assess the effectiveness, cost-effectiveness and acceptability of a pedometer-based walking intervention in inactive adults, delivered postally or through dedicated practice nurse physical activity (PA) consultations. Design Parallel three-arm trial, cluster randomised by household. Setting Seven London-based general practices. Participants A total of 11,015 people without PA contraindications, aged 45–75 years, randomly selected from practices, were invited. A total of 6399 people were non-responders, and 548 people self-reporting achieving PA guidelines were excluded. A total of 1023 people from 922 households were randomised to usual care (n = 338), postal intervention (n = 339) or nurse support (n = 346). The recruitment rate was 10% (1023/10,467). A total of 956 participants (93%) provided outcome data. Interventions Intervention groups received pedometers, 12-week walking programmes advising participants to gradually add ‘3000 steps in 30 minutes’ most days weekly and PA diaries. The nurse group was offered three dedicated PA consultations. Main outcome measures The primary and main secondary outcomes were changes from baseline to 12 months in average daily step counts and time in MVPA (in ≥ 10-minute bouts), respectively, from 7-day accelerometry. Individual resource-use data informed the within-trial economic evaluation and the Markov model for simulating long-term cost-effectiveness. Qualitative evaluations assessed nurse and participant views. A 3-year follow-up was conducted. Results Baseline average daily step count was 7479 [standard deviation (SD) 2671], average minutes per week in MVPA bouts was 94 minutes (SD 102 minutes) for those randomised. PA increased significantly at 12 months in both intervention groups compared with the control group, with no difference between interventions; additional steps per day were 642 steps [95% confidence interval (CI) 329 to 955 steps] for the postal group and 677 steps (95% CI 365 to 989 steps) for nurse support, and additional MVPA in bouts (minutes per week) was 33 minutes per week (95% CI 17 to 49 minutes per week) for the postal group and 35 minutes per week (95% CI 19 to 51 minutes per week) for nurse support. Intervention groups showed no increase in adverse events. Incremental cost per step was 19p and £3.61 per minute in a ≥ 10-minute MVPA bout for nurse support, whereas the postal group took more steps and cost less than the control group. The postal group had a 50% chance of being cost-effective at a £20,000 per quality-adjusted life-year (QALY) threshold within 1 year and had both lower costs [–£11M (95% CI –£12M to –£10M) per 100,000 population] and more QALYs [759 QALYs gained (95% CI 400 to 1247 QALYs)] than the nurse support and control groups in the long term. Participants and nurses found the interventions acceptable and enjoyable. Three-year follow-up data showed persistent intervention effects (nurse support plus postal vs. control) on steps per day [648 steps (95% CI 272 to 1024 steps)] and MVPA bouts [26 minutes per week (95% CI 8 to 44 minutes per week)]. Limitations The 10% recruitment level, with lower levels in Asian and socioeconomically deprived participants, limits the generalisability of the findings. Assessors were unmasked to the group. Conclusions A primary care pedometer-based walking intervention in 45- to 75-year-olds increased 12-month step counts by around one-tenth, and time in MVPA bouts by around one-third, with similar effects for the nurse support and postal groups, and persistent 3-year effects. The postal intervention provides cost-effective, long-term quality-of-life benefits. A primary care pedometer intervention delivered by post could help address the public health physical inactivity challenge. Future work Exploring different recruitment strategies to increase uptake. Integrating the Pedometer And Consultation Evaluation-UP (PACE-UP) trial with evolving PA monitoring technologies.

Published
2018

94. Engaging with feedback: How do students remediate errors on their weekly quiz

Author

Copeland, Cillian, Howard, Emma, Meehan, Maria, Parnell, Andrew, University College Dublin [Dublin] (UCD), INDRUM Network, University of Agder, and Sciencesconf.org, CCSD

Subjects
remediation of errors, students' practices at university level, [SHS.EDU]Humanities and Social Sciences/Education, [MATH.MATH-HO]Mathematics [math]/History and Overview [math.HO], [SHS.EDU] Humanities and Social Sciences/Education, [MATH.MATH-HO] Mathematics [math]/History and Overview [math.HO], feedback, assessment practices in university mathematics education
Abstract

International audience; Maths for Business is a first-year mathematics module for approximately 500 non-mathematics specialists. It has continuous assessment consisting of ten weekly quizzes, worth 40% of the final mark. In 2016/17, students who did not receive the maximum five marks on their weekly quiz were offered the opportunity to resubmit their quiz, with correction(s) and an explanation of their error(s), for one additional mark. We refer to this process as ‘remediation'. In this paper, we examine how students remediate their errors in order to identify features of a ‘good' remediation. These features are identification, description, and correction of errors. By analysing a subset of students (n=31), we observe that a student's quiz mark, and the cognitive level of the quiz question may impact the nature of the remediation provided.

Published
2018

96. Ligand cleavage enables formation of 1,2-ethanedithiol capped colloidal quantum dot solids

Author

Fan, James Z., primary, La Croix, Andrew D., additional, Yang, Zhenyu, additional, Howard, Emma, additional, Quintero-Bermudez, Rafael, additional, Levina, Larissa, additional, Jenkinson, Nicole M., additional, Spear, Nathan J., additional, Li, Yiying, additional, Ouellette, Olivier, additional, Lu, Zheng-Hong, additional, Sargent, Edward H., additional, and Macdonald, Janet E., additional

Published
2019
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98. A Comprehensive Review of Various Substrates Commonly Encountered at Crime Scenes and their Fluorescence Characteristics using the Polilight Flare® Plus 2

Author

Chapman, Brendan, Howard, Emma, Chapman, Brendan, and Howard, Emma

Abstract

Biological fluids are often present within a crime scene and can be a valuable source of DNA evidence; therefore the detection and identification of these fluids is critical. Alternative light sources are a presumptive testing tool that can be used to detect and enhance potential biological and non-biological staining, through the exploitation of the ultraviolet, visible light and infrared portion of the electromagnetic spectrum. Most biological fluids will emit fluorescence when excited by light from an alternative light source; however previous research has demonstrated that a range of non-biological products will also display fluorescence when viewed under alternative light sources, potentially causing confusion within a crime scene. Further investigation is required to develop a better understanding of the fluorescence displayed by biological and nonbiological products under a wide range of wavelengths and interference filters. This review will assess the current literature regarding the use of alternative light sources for the detection and visualisation of biological fluids, focusing on past studies that have recognised potential complications, such as the fluorescence of non-biological materials and the influence of background substrates on the detection of stains.

Published
2018

99. Quantifying participation in, and the effectiveness of, remediating assessment in a university mathematics module

Author

Howard, Emma, Meehan, Maria, Parnell, Andrew, Howard, Emma, Meehan, Maria, and Parnell, Andrew

Abstract

In Maths for Business, a large first-year mathematics module, the continuous assessment component comprises 10 weekly quizzes which combine to contribute 40% of the final module mark. If students did not receive the full five marks on their weekly quiz, they were provided with the opportunity to resubmit their corrected weekly quiz with an explanation of their error(s) for one additional mark. We refer to this process as ‘remediation’. Of the students who had the opportunity to remediate, ∼70% did. Through examining learning management system data, we show that the remediation process encouraged students to access module resources. Furthermore, by using a Bayesian hierarchical model to account for students’ level of participation, achievement and prior knowledge, we show that participation in the remediation process positively impacted the final examination marks of moderate to high achieving students (based on initial continuous assessment marks). However, participation in the remediation process provided limited benefit to low-achieving students. We conjecture this is because these students had not achieved a level of understanding whereby participation in the remediation process could progress their knowledge.

Published
2018

100. Social networks, geographic proximity, and firm performance in Viet Nam

Author

Howard, Emma

Subjects
social networks, L14, geographic proximity, L20, Viet Nam, ddc:330, manufacturing firms, D22
Abstract

This paper uses panel data to assess the relative importance of social networks and geographic proximity to micro, small, and medium enterprises in Viet Nam. The results suggest that a larger social network, and hiring employees mainly through social networks, are both correlated with higher value added per worker. The number of government officials and civil servants in a firm's network emerges as particularly important. When the quality of contacts is controlled for, firms with tighter social networks have, on average, higher value added per worker. The analysis of spatial networks reveals that firms with a lower percentage of customers and suppliers in the same district actually have higher value added per worker. The results suggest that for micro, small, and medium firms in Viet Nam, strong social networks are much more important than geographic proximity.

Published
2017

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