Academic literature on the topic 'Clinical learning'
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Journal articles on the topic "Clinical learning"
Jones-Schenk, Jan. "Redesigning Clinical Learning." Journal of Continuing Education in Nursing 52, no. 9 (September 2021): 402–3. http://dx.doi.org/10.3928/00220124-20210804-03.
Full textDovedi, Vishaal Kumar, and Anita Iyer. "Learning clinical leadership." Clinical Teacher 16, no. 3 (February 11, 2019): 290. http://dx.doi.org/10.1111/tct.13007.
Full textCarrion, C., P. Toran, A. Zamora, E. Balló, M. Quesada, A. Grau, A. Castro, et al. "Learning Clinical Communication." Procedia - Social and Behavioral Sciences 141 (August 2014): 85–89. http://dx.doi.org/10.1016/j.sbspro.2014.05.016.
Full textOhno-Machado, Lucila. "Clinical machine learning." Journal of Biomedical Informatics 38, no. 5 (October 2005): 345–46. http://dx.doi.org/10.1016/j.jbi.2005.05.009.
Full textPinnock, Ralph, and Paul Welch. "Learning clinical reasoning." Journal of Paediatrics and Child Health 50, no. 4 (December 23, 2013): 253–57. http://dx.doi.org/10.1111/jpc.12455.
Full textRosen, Dennis. "Learning Clinical Reasoning." JAMA 303, no. 3 (January 20, 2010): 277. http://dx.doi.org/10.1001/jama.2009.2030.
Full textGaniats, Theodore G. "Learning Clinical Reasoning." JAMA: The Journal of the American Medical Association 266, no. 22 (December 11, 1991): 3203. http://dx.doi.org/10.1001/jama.1991.03470220119044.
Full textParsell, G., and J. Bligh. "Contract learning, clinical learning and clinicians." Postgraduate Medical Journal 72, no. 847 (May 1, 1996): 284–89. http://dx.doi.org/10.1136/pgmj.72.847.284.
Full textCorriero, Nicola, Pierpaolo Di Bitonto, Teresa Roselli, Veronica Rossano, and Enrica Pesare. "Simulations of Clinical Cases for Learning in e-Health." International Journal of Information and Education Technology 4, no. 4 (2014): 378–82. http://dx.doi.org/10.7763/ijiet.2014.v4.434.
Full textLee, Cecilia S., and Aaron Y. Lee. "Clinical applications of continual learning machine learning." Lancet Digital Health 2, no. 6 (June 2020): e279-e281. http://dx.doi.org/10.1016/s2589-7500(20)30102-3.
Full textDissertations / Theses on the topic "Clinical learning"
Whiting, Michael Francis. "Learning in clinical contexts : clinical education in podiatry." Thesis, University of Sussex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262305.
Full textLocascio, Nicholas (Nicholas J. ). "Deep learning for clinical mammography screening." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113130.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
"June 2017." Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 37).
Breast cancer is the most common cancer among women worldwide. Today, the vast majority of breast cancers are diagnosed from screening mammography. Multiple randomized clinical studies have demonstrated that screening mammography can help reduce the number of deaths from breast cancer among women ages 40 to 74, especially for those over age 50 [4], and can provide women diagnosed with breast cancer more options for less aggressive treatment [7]. Screening mammography is the first entry into the funnel of clinical mammography. A screening mammogram can result in a suspicious finding, leading the patient to receive additional imaging, and even surgical biopsy if the additional imaging. Screening mammography, as the first part of this funnel, is a place for machine learning to have impact on the largest amount of patients. In this work, we apply machine learning models to tasks in clinical mammography such as density estimation, and Bi-Rads prediction.
by Nicholas Locascio.
M. Eng.
Walker, Rachel M. "Leading for Effective Clinical Education." Thesis, Griffith University, 2012. http://hdl.handle.net/10072/367404.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Nursing and Midwifery
Griffith Health
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Zhao, Yufan Kosorok Michael R. "Reinforcement learning design for cancer clinical trials." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2857.
Full textTitle from electronic title page (viewed Jun. 4, 2010). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biostatistics, School of Public Health." Discipline: Biostatistics; Department/School: Public Health.
Marrow, Carol Elizabeth. "Professional learning through clinical supervision in nursing." Thesis, University of Birmingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396514.
Full textBray, Farahnaz. "Student views on early clinical learning experiences." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86497.
Full textENGLISH ABSTRACT: Aim - The aim of this study was to explore second year medical students’ perceptions of their early clinical experiences with a view to improving curriculum development so as to enhance early clinical training programmes at Stellenbosch University (SU). Methodology - A qualitative, interpretive study, based on semi-structured focus group discussions with second year medical students was conducted in order to capture the relevant data that would provide information about their attitudes, feelings, beliefs and views on their early clinical learning experiences during their first year of studying medicine at SU. Thirty seven students participated in four focus group discussions after a process of selection of candidates using purposive sampling methods and stratification criteria to obtain the research sample. The interviews were moderated by an external facilitator, and were audiotaped and transcribed verbatim. The data transcripts were analysed and manually coded, and four broad categories with subthemes which illustrated the findings of the study, were identified and decided upon by the researcher and verified by the supervisor. Results - Early clinical exposure was generally positively perceived by students. It fostered a sense of vocation and feeling like real doctors, leaving students motivated and enhancing their learning interest. Early clinical skills training led to students’ professional development, acquiring the technical skills of a doctor, familiarisation with basic clinical terminology, and normal clinical findings which prepared them for later clinical studies. The new setting of practical learning in a simulated environment required students to adapt to small group learning and student clinical demonstrations which developed new learning styles and study skills. Some of the challenges that students encountered in the transition to clinical learning were, understanding the new subject of clinical medicine, having limited background knowledge to acquire basic clinical skills, and student clinical demonstrations. Although the strategy of peer physical examination was perceived to be effective, some ethical dilemmas emerged for students in terms of autonomy, and no opportunities available to practice on female models. Acting as a simulated patient proved to have both positive and negative outcomes on students’ skills acquisition. Factors that had a negative outcome on clinical skills learning were limited practice opportunities due to high student to teacher ratios per clinical session, and the variability of teaching content and practical techniques taught by various clinical tutors with different teaching strategies. The most stressful experience for students was the OSCE since it was a new method of assessment. Stress was attributed to uncertainty about the correct clinical content and techniques resulting from the teaching variability, while performance anxiety during the exam was related to inappropriate examiner behaviour. The OSCE was a positive learning experience because its format simulated the hospital setting which fostered students’ critical thinking abilities and time management. Conclusion - Early clinical exposure and practice have a great impact on junior medical students’ academic growth, and have positive learning outcomes. However, further development by the faculty in the areas of didactic skills, addressing the ethical issues related to student clinical demonstrations, and supporting students to enable a smooth transition to clinical learning will enhance and optimise their early clinical training.
Needham, Judith. "Best Practice in Clinical Facilitation of Undergraduate Nursing Students: The Perspectives of Clinical Facilitators." Thesis, Griffith University, 2015. http://hdl.handle.net/10072/365929.
Full textThesis (Professional Doctorate)
Doctor of Education (EdD)
School of Education and Professional Studies
Arts, Education and Law
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Cefo, Linda M. Dr. "Qualitative Study Exploring the Development of Clinical Reasoning in Nursing's Clinical Education Settings." Cleveland State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=csu1556456523899578.
Full textMsiska, Gladys. "Exploring the clinical learning experience : voices of Malawian undergraduate student nurses." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7772.
Full textNori, Nozomi. "Machine Learning Approaches for Personalized Clinical Risk Modeling." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225729.
Full textBooks on the topic "Clinical learning"
B, Wong John, and Kopelman Richard I, eds. Learning clinical reasoning. 2nd ed. Philadelphia: Wolters Kluwer Lippincott Williams & Wilkins Health, 2010.
Find full textI, Kopelman Richard, ed. Learning clinical reasoning. Baltimore: Williams & Wilkins, 1991.
Find full textClinical applications of learning theory. Hove: Psychology Press, 2011.
Find full textAbdulkadir, Ahmed, Seyed Mostafa Kia, Mohamad Habes, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, and Thomas Wolfers, eds. Machine Learning in Clinical Neuroimaging. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87586-2.
Full textStaartjes, Victor E., Luca Regli, and Carlo Serra, eds. Machine Learning in Clinical Neuroscience. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85292-4.
Full textAbdulkadir, Ahmed, Deepti R. Bathula, Nicha C. Dvornek, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, and Thomas Wolfers, eds. Machine Learning in Clinical Neuroimaging. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-17899-3.
Full textLindy, McAllister, ed. Facilitating learning in clinical settings. Cheltenham: S. Thornes, 1997.
Find full textOgier, Margaret E. Working and learning: The learning environment in clinical nursing. London: Scutari, 1989.
Find full textKassam, Tamiza A. Collective learning within nursing clinical groups. St. Catharines, Ont: Brock University, Faculty of Education, 2002.
Find full textScotland, Clinical Standards Board for. Learning disabilities: Clinical standards - February 2004. Edinburgh: Clinical Standards Board for Scotland, 2004.
Find full textBook chapters on the topic "Clinical learning"
Cunningham, Sheila. "Clinical Learning Environments." In Dimensions on Nursing Teaching and Learning, 33–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39767-8_3.
Full textParente, Rick. "Learning." In Encyclopedia of Clinical Neuropsychology, 1440–45. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1132.
Full textParente, Rick, Grace-Anna Chaney, and Maria St. Pierre. "Learning." In Encyclopedia of Clinical Neuropsychology, 1–6. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_1132-2.
Full textParente, Rick, Grace-Anna Chaney, and Maria St. Pierre. "Learning." In Encyclopedia of Clinical Neuropsychology, 1971–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1132.
Full textSingh, Nirbhay N., and Ivan L. Beale. "Learning Disabilities." In Applied Clinical Psychology, 525–53. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0983-3_21.
Full textHooper, Stephen R., and W. Grant Willis. "Clinical-Inferential Classification Models." In Learning Disability Subtyping, 41–61. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8810-4_3.
Full textCook, Lori G., and Sandra Bond Chapman. "Strategic Learning." In Encyclopedia of Clinical Neuropsychology, 3303–5. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_9012.
Full textCobia, Derin. "Machine Learning." In Encyclopedia of Clinical Neuropsychology, 2058–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_9058.
Full textParente, Rick. "Concept Learning." In Encyclopedia of Clinical Neuropsychology, 664. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1088.
Full textWarshowsky, Adam B. "Errorless Learning." In Encyclopedia of Clinical Neuropsychology, 972–73. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1090.
Full textConference papers on the topic "Clinical learning"
Mozumder, Meghdoot, Andreas Hauptmann, Simon R. Arridge, and Tanja Tarvainen. "Diffuse optical tomography utilizing model-based learning." In Clinical and Translational Biophotonics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/translational.2022.jtu3a.10.
Full textAlsulmi, Mohammad R., and Benjamin A. Carterette. "Learning to Rate Clinical Concepts Using Simulated Clinician Feedback." In IUI'17: 22nd International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3025171.3025232.
Full textSchwartz, Jessica, and Kenrick Cato. "Machine Learning Based Clinical Decision Support and Clinician Trust." In 2020 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2020. http://dx.doi.org/10.1109/ichi48887.2020.9374365.
Full textNemani, Arun, Suvranu De, and Xavier Intes. "Functional Brain Connectivity Distinguishes Surgical Skill Learning with Surgical Simulators." In Clinical and Translational Biophotonics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/translational.2018.jth3a.53.
Full textAfara, Isaac O., Jaakko K. Sarin, Simo Ojanen, Mikko Finnilä, Walter Herzog, Simo Saarakkala, Rami Korhonen, and Juha Töyräs. "Deep Learning Classification of Cartilage Integrity Using Near Infrared Spectroscopy." In Clinical and Translational Biophotonics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/translational.2018.jtu3a.27.
Full textRajendran, Praveenbalaji, and Manojit Pramanik. "High frame rate multi-transducer photoacoustic tomography with deep learning." In Clinical and Translational Biophotonics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/translational.2022.jtu3a.11.
Full textPitkäaho, Tomi, Aki Manninen, and Thomas J. Naughton. "Temporal Deep Learning Classification of Digital Hologram Reconstructions of Multicellular Samples." In Clinical and Translational Biophotonics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/translational.2018.jw3a.14.
Full textKrzyston, Jakob, Seung Yup Lee, Erin Buckley, and Eva Dyer. "Learning Biomarkers of Disease from Non-Invasive Measurements of Blood Flow." In Clinical and Translational Biophotonics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/translational.2018.jw3a.48.
Full textLi, Guo, Zhaoqiang Wang, Hao Zhang, Lanxin Zhu, and Peng Fei. "Deep-learning light-field microscopy with improved resolution and reconstruction speed." In Clinical and Translational Biophotonics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/translational.2020.jw5a.1.
Full textCosta, Paloma Casteleiro, Nischita Kaza, and Francisco E. Robles. "Deep Learning Enabled Single-Capture Epi-illumination Tomographic Quantitative Phase Imaging." In Clinical and Translational Biophotonics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/translational.2022.jm3a.19.
Full textReports on the topic "Clinical learning"
Clement, Timothy, and Brett Vaughan. Evaluation of a mobile learning platform for clinical supervision. University of Melbourne, 2021. http://dx.doi.org/10.46580/124369.
Full textMcDonald, Claudia L. Pulse!! The Virtual Clinical Learning Lab and Center of Excellence. Fort Belvoir, VA: Defense Technical Information Center, August 2011. http://dx.doi.org/10.21236/ada549718.
Full textArtino, Jr, Cleary Anthony R., Dong Timothy J., Hemmer Ting, Durning Paul A., and Steven J. Exploring Clinical Reasoning in Novices: A Self-Regulated Learning Microanalytic Approach. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada587612.
Full textWeiss, Kevin, Morgan Passiment, Laura Riordan, and Robin Wagner. Achieving the Optimal Interprofessional Clinical Learning Environment: Proceedings From an NCICLE Symposium. National Collaborative for Improving the Clincial Learning Environment, January 2019. http://dx.doi.org/10.33385/ncicle.0002.
Full textFatima, Sahar, Mohamad Nabil Mohd Noor, Vinod Pallath, Foong Chanchoong, and Hong Wei-Han. Evaluating the effectiveness of instructional strategies in promoting self-regulated learning during clinical clerkship years. A protocol for systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2023. http://dx.doi.org/10.37766/inplasy2023.1.0065.
Full textKoh, NJ, R. Wagner, R. Newton, KW Hirsch, CM Kuhn, KB Weiss, and on behalf of the CLER Evaluation Committee and the CLER Program. CLER National Report of Findings 2022: The COVID-19 Pandemic and Its Impact on the Clinical Learning Environment. ACGME, December 2022. http://dx.doi.org/10.35425/acgme/0009.
Full textWan Brown, Jackie, Katherine Rogers, and Alys Young. What is the evidence underpinning clinical assessment of mental health of deaf adults with learning disabilities: A scoping review protocol. INPLASY - INPLASY International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0007.
Full textWang, Yingxuan, Cheng Yan, and Liqin Zhao. The value of radiomics-based machine learning for hepatocellular carcinoma after TACE: a systematic evaluation and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0100.
Full textZachry, Anne, J. Flick, and S. Lancaster. Tune Up Your Teaching Toolbox! University of Tennessee Health Science Center, 2016. http://dx.doi.org/10.21007/chp.ot.fp.2016.0001.
Full textMillington, Kerry. COVID-19 Health Evidence Summary No.117. Institute of Development Studies, March 2021. http://dx.doi.org/10.19088/k4d.2021.041.
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