Software-Assisted Transcribing for Qualitative Interviews

2015 ◽  
pp. 231-254
Author(s):  
Taghreed Justinia
Keyword(s):  
Software Package ◽  
Large Scale ◽  
Qualitative Interviews ◽  
Real Life ◽  
Practical Advice ◽  
Software Packages ◽  
Hands On ◽  
Research Interviews ◽  
Pros And Cons ◽  
Depth Interviews

This chapter introduces a guide to transcribing qualitative research interviews assisted by digital transcription software. It also provides practical advice on transcribing methods, conventions, and options. It is useful in its exploration of the challenges involved with transcribing, while it offers detailed solutions and advice for the novice researcher. The chapter also addresses key concerns, like the time it takes to transcribe, transcription tools, and digital versus analogue recordings. As a method chapter based on experiences from a case, it takes on a practical approach by demonstrating the benefits of data analysis software packages with examples and screenshots on how to specifically use the software package Express Scribe. The pros and cons of using a transcriptionist are also discussed. A real transcript is presented in the chapter, and the steps involved with developing and formatting it are offered in detail. The guidelines suggested in this chapter are concentrated on the pragmatic hands-on experience of a researcher with examples from a real life large-scale qualitative study based on in-depth interviews. The significance of transcribing within the analytical process and the methodological insights of using Express Scribe eventually emerge as a developing concept from this work.

scholarly journals Conducting In-Depth Interviews via Mobile Phone with Persons with Common Mental Disorders and Multimorbidity: The Challenges and Advantages as Experienced by Participants and Researchers

2021 ◽  
Vol 18 (22) ◽  
pp. 11828
Author(s):  
Azadé Azad ◽  
Elisabet Sernbo ◽  
Veronica Svärd ◽  
Lisa Holmlund ◽  
Elisabeth Björk Brämberg
Keyword(s):  
Mental Disorders ◽  
Mobile Phone ◽  
Common Mental Disorders ◽  
Qualitative Interviews ◽  
Self Disclosure ◽  
Telephone Interviews ◽  
Illness Experiences ◽  
Research Interviews ◽  
Field Notes ◽  
Depth Interviews

Qualitative interviews are generally conducted in person. As the coronavirus pandemic (COVID-19) prevents in-person interviews, methodological studies which investigate the use of the telephone for persons with different illness experiences are needed. The aim was to explore experiences of the use of telephone during semi-structured research interviews, from the perspective of participants and researchers. Data were collected from mobile phone interviews with 32 individuals who had common mental disorders or multimorbidity which were analyzed thematically, as well as field notes reflecting researchers’ experiences. The findings reveal several advantages of conducting interviews using mobile phones: flexibility, balanced anonymity and power relations, as well as a positive effect on self-disclosure and emotional display (leading to less emotional work and social responsibility). Challenges included the loss of human encounter, intense listening, and worries about technology, as well as sounds or disturbances in the environment. However, the positive aspects of not seeing each other were regarded as more important. In addition, we present some strategies before, during, and after conducting telephone interviews. Telephone interviews can be a valuable first option for data collection, allowing more individuals to be given a fair opportunity to share their experiences.

The Role of Teacher Self-Strategies in First Year Teacher Experience and Teacher Socialisation

2013 ◽  
pp. 654-663
Author(s):  
Ekaterina Kozina ◽  
Aidan Seery ◽  
Andrew Loxley
Keyword(s):  
Professional Practice ◽  
Mixed Methods Research ◽  
Teaching And Learning ◽  
Large Scale ◽  
Qualitative Interviews ◽  
Real Life ◽  
Primary Teachers ◽  
Classroom Teaching ◽  
First Year ◽  
The Republic

It is recognised that the first year of professional practice of teachers, also known as an induction year, has far reaching implications for their subsequent teaching career. This chapter discusses the findings of a large scale mixed-methods research project (2006-2010) conducted on the socialisation experiences of beginning primary teachers in the Republic of Ireland. In detail, the project was concerned with real life experiences of teachers as they progress through their first year of professional practice. The data on which the chapter reports was collected by means of a postal questionnaire to 1635 teachers and 52 in-depth qualitative interviews. The authors start the discussion by providing a rationale for this research and a broad overview of the teaching challenges faced by beginning teachers. Consideration is given to the ways in which first year teachers generate knowledge and meaning from an interaction between their experiences of classroom teaching and their approaches to address challenges they encounter. More specifically, the chapter discusses teacher self-strategies to find solutions to challenges to their practice and the ways in which collaboration and interaction with colleagues promotes classroom environments conducive to more effective teaching and learning. Lastly, some insight is provided into the models of induction supports available in primary schools and their potential to transform the experience of classroom teaching for beginning primary teachers.

Hiding in plain sight: the emergence of modern dairy farms in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saule Burkitbayeva ◽  
Emma Janssen ◽  
Johan Swinnen
Keyword(s):  
Large Scale ◽  
Qualitative Data ◽  
Qualitative Interviews ◽  
Dairy Farms ◽  
Value Chains ◽  
Snowball Sampling ◽  
Smallholder Farms ◽  
Content Type ◽  
Use Of Technology ◽  
Depth Interviews

PurposeThis paper provides one of the first and most detailed accounts of the large modern dairy farms that are emerging in the dairy sector in India. Qualitative interviews are used to understand how these farms differ from their traditional smallholder counterparts and how well integrated they are into the value chains.Design/methodology/approachSnowball sampling was used to identify large farmers. In total, 49 in-depth interviews were conducted with large commercial modern farms in Punjab. A detailed description of the main characteristics of these modern dairy farms is provided. Data from previous studies conducted in Punjab is used to compare the new farms with traditional smallholder farms.FindingsThe modern dairy farms are much more advanced in their use of technology compared to their traditional counterparts. These large commercial modern farms are very well integrated into the value chains. They often, but not exclusively, sell milk to formal supply chains, sometimes on a contractual basis.Originality/valueMost of the literature on the Indian dairy sector focuses on smallholders. However, understanding and acknowledging the emergence of modern dairy farms is very important in understanding the development of value chains not only in the dairy sector in India, but in domestic food sectors in developing countries in general. This qualitative data analysis is a necessary first step if more large-scale representative information is to be collected in the future.

Does high-variation training facilitate transfer of training in paediatric transthoracic echocardiography?

2020 ◽  
pp. 1-7
Author(s):  
Colin J. McMahon ◽  
Sarah Gallagher ◽  
Adam James ◽  
Aoife Deery ◽  
Mark Rhodes ◽  
...  
Keyword(s):  
Transfer Of Training ◽  
Assessment Tool ◽  
Qualitative Interviews ◽  
Training Group ◽  
Variation Theory ◽  
Cardiac Lesion ◽  
Cumulative Score ◽  
Hands On ◽  
Performance Report ◽  
Technical Proficiency

Abstract Background: Factors that facilitate transfer of training in paediatric echocardiography remain poorly understood. This study assessed whether high-variation training facilitated successful transfer in paediatric echocardiography. Methods: A mixed-methods study of transfer of technical and interpretive skill application amongst postgraduate trainees. Trainees were randomised to a low or high-variation training group. After a period of 8 weeks intensive echocardiography training, we video-recorded how trainees completed an echocardiogram in a complex cardiac lesion not previously encountered. Blinded quantitative analysis and scoring of trainee performance (echocardiogram performance, report, and technical proficiency) were performed using a validated assessment tool by a blinded cardiologist and senior cardiac physiologist. Qualitative interviews of the trainees were recorded to ascertain trainee experiences during the training and transfer process. Results: Sixteen trainees were enrolled in the study. For the cumulative score for all three components tested (echocardiogram performance, report, and technical proficiency), χ2 = 8.223, p = .016, which showed the high-variation group outperformed the low-variation group. Two common themes which assisted in the transfer emerged from interviews are as follows: (1) use of strategies described in variation theory to describe abnormal hearts, (2) the use of formative live feedback from trainers during hands-on training. Conclusion: Training strategies exposing trainees to high-variation training may aid transfer of paediatric echocardiography skills.

scholarly journals Neural methods for effective, efficient, and exposure-aware information retrieval

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
Bhaskar Mitra
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Web Search ◽  
Real Life ◽  
Inverted Index ◽  
Information Need ◽  
Product Model ◽  
Performance Improvements ◽  
Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

scholarly journals Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

2021 ◽  
Author(s):  
Gianluca Bardaro ◽  
Alessio Antonini ◽  
Enrico Motta
Keyword(s):  
Large Scale ◽  
Daily Life ◽  
Elderly Care ◽  
Real Life ◽  
Robotic Platform ◽  
Holistic View ◽  
Personal Assistants ◽  
Healthcare Interventions ◽  
The Impact ◽  
The Eu

AbstractOver the last two decades, several deployments of robots for in-house assistance of older adults have been trialled. However, these solutions are mostly prototypes and remain unused in real-life scenarios. In this work, we review the historical and current landscape of the field, to try and understand why robots have yet to succeed as personal assistants in daily life. Our analysis focuses on two complementary aspects: the capabilities of the physical platform and the logic of the deployment. The former analysis shows regularities in hardware configurations and functionalities, leading to the definition of a set of six application-level capabilities (exploration, identification, remote control, communication, manipulation, and digital situatedness). The latter focuses on the impact of robots on the daily life of users and categorises the deployment of robots for healthcare interventions using three types of services: support, mitigation, and response. Our investigation reveals that the value of healthcare interventions is limited by a stagnation of functionalities and a disconnection between the robotic platform and the design of the intervention. To address this issue, we propose a novel co-design toolkit, which uses an ecological framework for robot interventions in the healthcare domain. Our approach connects robot capabilities with known geriatric factors, to create a holistic view encompassing both the physical platform and the logic of the deployment. As a case study-based validation, we discuss the use of the toolkit in the pre-design of the robotic platform for an pilot intervention, part of the EU large-scale pilot of the EU H2020 GATEKEEPER project.

scholarly journals COVID-19 and doctor emigration: the case of Ireland

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Niamh Humphries ◽  
Jennifer Creese ◽  
John-Paul Byrne ◽  
John Connell
Keyword(s):  
Health System ◽  
Large Scale ◽  
Qualitative Interviews ◽  
Snowball Sampling ◽  
Contact Potential ◽  
Work From Home ◽  
Travel Restrictions ◽  
Hospital Doctors ◽  
June July ◽  
Global Travel

Abstract Background Since the 2008 recession, Ireland has experienced large-scale doctor emigration. This paper seeks to ascertain whether (and how) the COVID-19 pandemic might disrupt or reinforce existing patterns of doctor emigration. Method This paper draws on qualitative interviews with 31 hospital doctors in Ireland, undertaken in June–July 2020. As the researchers were subject to a government mandated work-from-home order at that time, they utilised Twitter™ to contact potential respondents (snowball sampling); and conducted interviews via Zoom™ or telephone. Findings Two cohorts of doctors were identified; COVID Returners (N = 12) and COVID Would-be Emigrants (N = 19). COVID Returners are Irish-trained emigrant doctors who returned to Ireland in March 2020, just as global travel ground to a halt. They returned to be closer to home and in response to a pandemic-related recruitment call issued by the Irish government. COVID Would-be Emigrants are hospital doctors considering emigration. Some had experienced pandemic-related disruptions to their emigration plans as a result of travel restrictions and border closures. However, most of the drivers of emigration mentioned by respondents related to underlying problems in the Irish health system rather than to the pandemic, i.e. a culture of medical emigration, poor working conditions and the limited availability of posts in the Irish health system. Discussion/conclusion This paper illustrates how the pandemic intensified and reinforced, rather than radically altered, the dynamics of doctor emigration from Ireland. Ireland must begin to prioritise doctor retention and return by developing a coherent policy response to the underlying drivers of doctor emigration.

scholarly journals Assessment of Wheelchair Propulsion Performance in an Immersive Virtual Reality Simulator

2021 ◽  
Vol 18 (15) ◽  
pp. 8016
Author(s):  
Yu-Sheng Yang ◽  
Alicia M. Koontz ◽  
Yu-Hsuan Hsiao ◽  
Cheng-Tang Pan ◽  
Jyh-Jong Chang
Keyword(s):  
Virtual Reality ◽  
Spinal Cord Injuries ◽  
Qualitative Interviews ◽  
Real Life ◽  
Structured Interview ◽  
Wheelchair Propulsion ◽  
Subjective Data ◽  
Wheelchair Users ◽  
Head Mounted Display ◽  
Propulsion Performance

Maneuvering a wheelchair is an important necessity for the everyday life and social activities of people with a range of physical disabilities. However, in real life, wheelchair users face several common challenges: articulate steering, spatial relationships, and negotiating obstacles. Therefore, our research group has developed a head-mounted display (HMD)-based intuitive virtual reality (VR) stimulator for wheelchair propulsion. The aim of this study was to investigate the feasibility and efficacy of this VR stimulator for wheelchair propulsion performance. Twenty manual wheelchair users (16 men and 4 women) with spinal cord injuries ranging from T8 to L2 participated in this study. The differences in wheelchair propulsion kinematics between immersive and non-immersive VR environments were assessed using a 3D motion analysis system. Subjective data of the HMD-based intuitive VR stimulator were collected with a Presence Questionnaire and individual semi-structured interview at the end of the trial. Results indicated that propulsion performance was very similar in terms of start angle (p = 0.34), end angle (p = 0.46), stroke angle (p = 0.76), and shoulder movement (p = 0.66) between immersive and non-immersive VR environments. In the VR episode featuring an uphill journey, an increase in propulsion speed (p < 0.01) and cadence (p < 0.01) were found, as well as a greater trunk forward inclination (p = 0.01). Qualitative interviews showed that this VR simulator made an attractive, novel impression and therefore demonstrated the potential as a tool for stimulating training motivation. This HMD-based intuitive VR stimulator can be an effective resource to enhance wheelchair maneuverability experiences.

scholarly journals Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Christos Makris ◽  
Georgios Pispirigos
Keyword(s):  
Social Networks ◽  
Graph Partitioning ◽  
Large Scale ◽  
Real Life ◽  
Information Networks ◽  
Digital Marketing ◽  
Partitioning Problems ◽  
Iterative Solutions ◽  
Community Extraction ◽  
Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

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