Dataset Information

Clarifying learning experiences in student-run clinics: a qualitative study.

ABSTRACT:

Background

Student-run clinics (SRCs) are outpatient clinics run and organized by undergraduate medical students. While these clinics offer participating students multiple learning opportunities, little is known about how participation in an SRC contributes to learning and how this learning is influenced.

Methods

In this qualitative clarification study, we conducted semi-structured interviews with a purposive sample of 20 students and student-coordinators participating in our learner-centred SRC (LC-SRC), to gain in-depth insight into their experiences and learning. These interviews were analysed using Glaser's approach to grounded theory.

Results

Analysis revealed that responsibility, authenticity, and collaboration described how SRC participation contribute to learning. Responsibility encompassed the responsibility students had for their patients and the responsibility that the student coordinators had for the students. Authenticity reflected the context and tasks in the LC-SRC. Collaboration covered collaboration with other students, with student coordinators, and with clinical supervisors. These three themes are interrelated, and together enhanced motivation and promoted patient-centred learning in both the LC-SRC and the regular curriculum.

Conclusions

Learning in an LC-SRC is highly dependent on students' feelings of responsibility for real authentic tasks and is stimulated by extensive collaboration with fellow students and supervising doctors.

SUBMITTER: Schutte T

PROVIDER: S-EPMC6204044 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

Clarifying learning experiences in student-run clinics: a qualitative study.

Schutte Tim T Tichelaar Jelle J Donker Erik E Richir Milan C MC Westerman Michiel M van Agtmael Michiel A MA

BMC medical education 20181026 1

<h4>Background</h4>Student-run clinics (SRCs) are outpatient clinics run and organized by undergraduate medical students. While these clinics offer participating students multiple learning opportunities, little is known about how participation in an SRC contributes to learning and how this learning is influenced.<h4>Methods</h4>In this qualitative clarification study, we conducted semi-structured interviews with a purposive sample of 20 students and student-coordinators participating in our lear ...[more]

PMID: 30367661

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Project description:BackgroundRecently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML.ObjectiveThis study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems.MethodsTo gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care-specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process.ResultsWith the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process.ConclusionsMany clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.

Dataset Information

Clarifying learning experiences in student-run clinics: a qualitative study.

Background

Methods

Results

Conclusions

Publications

Clarifying learning experiences in student-run clinics: a qualitative study.

Similar Datasets

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