The ELIXIR-EXCELERATE Train-the-Trainer pilot programme: empower researchers to deliver high-quality training

One of the main goals of the ELIXIR-EXCELERATE project from the European Union’s Horizon 2020 programme is to support a pan-European training programme to increase bioinformatics capacity and competency across ELIXIR Nodes. To this end, a Train-the-Trainer (TtT) programme has been developed by the TtT subtask of EXCELERATE’s Training Platform, to try to expose bioinformatics instructors to aspects of pedagogy and evidence-based learning principles, to help them better design, develop and deliver high-quality training in future. As a first step towards such a programme, an ELIXIR-EXCELERATE TtT (EE-TtT) pilot was developed, drawing on existing ‘instructor training’ models, using input both from experienced instructors and from experts in bioinformatics, the cognitive sciences and educational psychology. This manuscript describes the process of defining the pilot programme, illustrates its goals, structure and contents, and discusses its outcomes. From Jan 2016 to Jan 2017, we carried out seven pilot EE-TtT courses (training more than sixty new instructors), collaboratively drafted the training materials, and started establishing a network of trainers and instructors within the ELIXIR community. The EE-TtT pilot represents an essential step towards the development of a sustainable and scalable ELIXIR TtT programme. Indeed, the lessons learned from the pilot, the experience gained, the materials developed, and the analysis of the feedback collected throughout the seven pilot courses have both positioned us to consolidate the programme in the coming years, and contributed to the development of an enthusiastic and expanding ELIXIR community of instructors and trainers.

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Developing a Training Program for Digital Literacy Coaches for Older Adults: Lessons Learned from the Train-the-Trainer Program

Journal of Education and Training Studies ◽

10.11114/jets.v8i11.5002 ◽

2020 ◽

Vol 8 (11) ◽

pp. 62

Author(s):

Jacqueline Mary McGinty

Keyword(s):

Older Adults ◽

Training Program ◽

Lifelong Learning ◽

Digital Literacy ◽

Lessons Learned ◽

Positive Health ◽

Train The Trainer ◽

Critical Issues ◽

Technology Strategies ◽

Learning Principles

Social isolation and loneliness are critical issues facing older adults. The COVID-19 pandemic increased isolation among this population, and digital technologies are considered a significant lifeline for older adults adhering to social distancing guidelines. Research has shown that lifelong learning and maintaining social connection during the senior years leads to positive health outcomes. The digital literacy training program addresses isolation and loneliness issues and promotes lifelong learning among older adults. The program's delivery is a train-the-trainer (TT) model. In the TT model for this study, volunteers facilitated eight digital technology lessons after receiving training on adult learning principles, working with older adults, and teaching technology strategies. This article aims to share the results from the TT process and the curriculum implementation from the volunteer trainers' perspectives, called coaches, who participated in the pilot program. The coaches' feedback and the observations made by the researchers offer essential insights into the design and development of a TT model for a technology training program for older adults. The findings suggest improvements for the curriculum and the TT model, including equipping the coaches with tablets at the beginning of the training, providing extended video conference practice, increasing technical support on-site, and expanding step by step instructions for training guides.

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Delivering Innovative RDM Training: The immersiveInformatics Pilot Programme

International Journal of Digital Curation ◽

10.2218/ijdc.v9i1.318 ◽

2014 ◽

Vol 9 (1) ◽

pp. 313-323 ◽

Cited By ~ 3

Author(s):

Anna Shadbolt ◽

Leo Konstantelos ◽

Liz Lyon ◽

Marieke Guy

Keyword(s):

Data Management ◽

Training Programme ◽

Professional Staff ◽

Research Data ◽

Lessons Learned ◽

Single Institution ◽

Pilot Programme ◽

Research Data Management ◽

Capability Building ◽

The University

This paper presents the findings, lessons learned and next steps associated with the implementation of the immersiveInformatics pilot: a distinctive research data management (RDM) training programme designed in collaboration between UKOLN Informatics and the Library at the University of Melbourne, Australia. The pilot aimed to equip a broad range of academic and professional staff roles with RDM skills as a key element of capacity and capability building within a single institution.

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In silico imaging clinical trials: cheaper, faster, better, safer, and more scalable

Trials ◽

10.1186/s13063-020-05002-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Aldo Badano

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Medical Devices ◽

In Silico ◽

Lessons Learned ◽

Patient Access ◽

High Quality ◽

Virtual Imaging ◽

Regulatory Evaluation

AbstractImaging clinical trials can be burdensome and often delay patient access to novel, high-quality medical devices. Tools for in silico imaging trials have significantly improved in sophistication and availability. Here, I describe some of the principal advantages of in silico imaging trials and enumerate five lessons learned during the design and execution of the first all-in silico virtual imaging clinical trial for regulatory evaluation (the VICTRE study).

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Evaluation of a staff training programme to reimplement a comprehensive health assessment

BMJ Open Quality ◽

10.1136/bmjoq-2018-000353 ◽

2018 ◽

Vol 7 (4) ◽

pp. e000353 ◽

Cited By ~ 1

Author(s):

Luke A Turcotte ◽

Jake Tran ◽

Joshua Moralejo ◽

Nancy Curtin-Telegdi ◽

Leslie Eckel ◽

...

Keyword(s):

Data Quality ◽

Quality Assessment ◽

Health Information ◽

Health Assessment ◽

Training Programme ◽

Care Planning ◽

Assessment Framework ◽

High Quality ◽

Data Quality Assessment ◽

Comprehensive Health

BackgroundHealth information systems with applications in patient care planning and decision support depend on high-quality data. A postacute care hospital in Ontario, Canada, conducted data quality assessment and focus group interviews to guide the development of a cross-disciplinary training programme to reimplement the Resident Assessment Instrument–Minimum Data Set (RAI-MDS) 2.0 comprehensive health assessment into the hospital’s clinical workflows.MethodsA hospital-level data quality assessment framework based on time series comparisons against an aggregate of Ontario postacute care hospitals was used to identify areas of concern. Focus groups were used to evaluate assessment practices and the use of health information in care planning and clinical decision support. The data quality assessment and focus groups were repeated to evaluate the effectiveness of the training programme.ResultsInitial data quality assessment and focus group indicated that knowledge, practice and cultural barriers prevented both the collection and use of high-quality clinical data. Following the implementation of the training, there was an improvement in both data quality and the culture surrounding the RAI-MDS 2.0 assessment.ConclusionsIt is important for facilities to evaluate the quality of their health information to ensure that it is suitable for decision-making purposes. This study demonstrates the use of a data quality assessment framework that can be applied for quality improvement planning.

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Learning from Disagreement: A Survey

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.12752 ◽

2021 ◽

Vol 72 ◽

pp. 1385-1470

Author(s):

Alexandra N. Uma ◽

Tommaso Fornaciari ◽

Dirk Hovy ◽

Silviu Paun ◽

Barbara Plank ◽

...

Keyword(s):

Language Processing ◽

Gold Standard ◽

Training Methods ◽

High Quality ◽

Training Models ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

Growing Body ◽

Research Questions ◽

Speech Tagging

Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer evidence that humans disagree, from objective tasks such as part-of-speech tagging to more subjective tasks such as classifying an image or deciding whether a proposition follows from certain premises. While most learning in artificial intelligence (AI) still relies on the assumption that a single (gold) interpretation exists for each item, a growing body of research aims to develop learning methods that do not rely on this assumption. In this survey, we review the evidence for disagreements on NLP and CV tasks, focusing on tasks for which substantial datasets containing this information have been created. We discuss the most popular approaches to training models from datasets containing multiple judgments potentially in disagreement. We systematically compare these different approaches by training them with each of the available datasets, considering several ways to evaluate the resulting models. Finally, we discuss the results in depth, focusing on four key research questions, and assess how the type of evaluation and the characteristics of a dataset determine the answers to these questions. Our results suggest, first of all, that even if we abandon the assumption of a gold standard, it is still essential to reach a consensus on how to evaluate models. This is because the relative performance of the various training methods is critically affected by the chosen form of evaluation. Secondly, we observed a strong dataset effect. With substantial datasets, providing many judgments by high-quality coders for each item, training directly with soft labels achieved better results than training from aggregated or even gold labels. This result holds for both hard and soft evaluation. But when the above conditions do not hold, leveraging both gold and soft labels generally achieved the best results in the hard evaluation. All datasets and models employed in this paper are freely available as supplementary materials.

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The Smart Condo Project

Advances in Medical Technologies and Clinical Practice - E-Health, Assistive Technologies and Applications for Assisted Living ◽

10.4018/978-1-60960-469-1.ch013 ◽

2011 ◽

pp. 289-314 ◽

Cited By ~ 1

Author(s):

Nicholas M. Boers ◽

David Chodos ◽

Pawel Gburzynski ◽

Lisa Guirguis ◽

Jianzhao Huang ◽

...

Keyword(s):

Sensor Network ◽

Assisted Living ◽

Universal Design ◽

Ambient Assisted Living ◽

Lessons Learned ◽

Wireless Sensor ◽

High Quality ◽

Quality Healthcare ◽

Living Space ◽

Smart Healthcare

Most would agree that older adults want affordable, high-quality healthcare that enables them to live independently longer and in their own homes. To this end, ambient assisted living environments have been developed that are able to non-intrusively monitor the health of people at-home and to provide them with improved care. The authors have designed an environment, the Smart Condo, to support seniors and rehabilitating patients. They have embedded a wireless sensor network into a model living space, which incorporates universal design principles. Information from the sensor network is archived in a server, which supports a range of views via APIs. One such view is a virtual world, which is realistic and intuitive, while remaining non-intrusive. This chapter examines computing technologies for smart healthcare-related environments and the needs of elderly patients. It discusses the Smart Condo architecture, reviews key research challenges, and presents the lessons learned through the project.

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The contraction history of the muscle and strength change: lessons learned from unilateral training models

Physiological Measurement ◽

10.1088/1361-6579/ab516c ◽

2020 ◽

Vol 41 (1) ◽

pp. 01TR01

Author(s):

Zachary W Bell ◽

Vickie Wong ◽

Robert W Spitz ◽

Raksha N Chatakondi ◽

Ricardo Viana ◽

...

Keyword(s):

Lessons Learned ◽

Training Models ◽

Strength Change ◽

History Of ◽

Unilateral Training

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Adopting a lay navigator training programme in primary care

Health Education Journal ◽

10.1177/0017896920959364 ◽

2020 ◽

pp. 001789692095936

Author(s):

Darene Toal-Sullivan ◽

Manon Lemonde ◽

Alain P Gauthier ◽

Simone Dahrouge

Keyword(s):

Primary Care ◽

Planning Process ◽

Core Competencies ◽

Training Programme ◽

Lessons Learned ◽

Patient Navigator ◽

Face To Face ◽

Patient Navigators ◽

Evaluation Feedback ◽

Training Expectations

Introduction: There is growing interest in the role and use of patient navigators within the health care system. Currently, qualifications and training expectations documented in the literature vary tremendously depending on context and patient population. This paper details the theoretical and pedagogical principles used to develop, implement and evaluate a training programme for lay patient navigators working in a primary care setting. Methods: The planning process involved (a) conducting an educational needs assessment, (b) identifying the theory underpinning the curriculum, (c) developing learning objectives and teaching strategies, (d) formulating evaluation methods, (e) implementing the programme and (f) refining the curriculum based on evaluation feedback and lessons learned. The training programme was first implemented in May 2017 and has evolved over the past 3 years based on our observations and feedback from the programme participants. Results: The training programme involves a total of 25 hours of online and face-to-face education sessions, and ongoing community mentorship from experienced navigators. All training components are rooted in theoretical principles and proven pedagogical approaches. The knowledge, skills and abilities acquired are also tied to core competencies of the role of lay patient navigator. Conclusion: The development of this lay navigator training programme was carefully designed with evidence-based competencies and practical realities to ensure rigour in preparing and supporting navigators’ work in primary care settings.

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Reactor performance, system reliability, instrumentation and control

EPJ Nuclear Sciences & Technologies ◽

10.1051/epjn/2019017 ◽

2020 ◽

Vol 6 ◽

pp. 43

Author(s):

Andreas Schumm ◽

Madalina Rabung ◽

Gregory Marque ◽

Jary Hamalainen

Keyword(s):

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Lessons Learned ◽

Software Systems ◽

Reactor Performance ◽

Horizon 2020 ◽

Term Operation ◽

In The Beginning ◽

Electrical Cables

We present a cross-cutting review of three on-going Horizon 2020 projects (ADVISE, NOMAD, TEAM CABLES) and one already finished FP7 project (HARMONICS), which address the reliability of safety-relevant components and systems in nuclear power plants, with a scope ranging from the pressure vessel and primary loop to safety-critical software systems and electrical cables. The paper discusses scientific challenges faced in the beginning and achievements made throughout the projects, including the industrial impact and lessons learned. Two particular aspects highlighted concern the way the projects sought contact with end users, and the balance between industrial and academic partners. The paper concludes with an outlook on follow-up issues related to the long term operation of nuclear power plants.

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