Background: The numbers of learners seeking placements in general practice is rapidly increasing as an ageing workforce impacts on General Practitioner availability. The traditional master apprentice model that involves one-to-one teaching is therefore leading to supervision capacity constraints. Vertically integrated (VI) models may provide a solution. Shared learning, in which multiple levels of learners are taught together in the same session, is one such model. This study explored stakeholders' perceptions of shared learning in general practices in northern NSW, Australia.
Methods: A qualitative research method, involving individual semi-structured interviews with GP supervisors, GP registrars, Prevocational General Practice Placements Program trainees, medical students and practice managers situated in nine teaching practices, was used to investigate perceptions of shared learning practices. A thematic analysis was conducted on 33 transcripts by three researchers.
Results: Participants perceived many benefits to shared learning including improved collegiality, morale, financial rewards, and better sharing of resources, knowledge and experience. Additional benefits included reduced social and professional isolation, and workload. Perceived risks of shared learning included failure to meet the individual needs of all learners. Shared learning models were considered unsuitable when learners need to: receive remediation, address a specific deficit or immediate learning needs, learn communication or procedural skills, be given personalised feedback or be observed by their supervisor during consultations. Learners' acceptance of shared learning appeared partially dependent on their supervisors' small group teaching and facilitation skills.
Conclusions: Shared learning models may partly address supervision capacity constraints in general practice, and bring multiple benefits to the teaching environment that are lacking in the one-to-one model. However, the risks need to be managed appropriately, to ensure learning needs are met for all levels of learners. Supervisors also need to consider that one-to-one teaching may be more suitable in some instances. Policy makers, medical educators and GP training providers need to ensure that quality learning outcomes are achieved for all levels of learners. A mixture of one-to-one and shared learning would address the benefits and downsides of each model thereby maximising learners' learning outcomes and experiences.
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http://dx.doi.org/10.1186/1471-2296-14-144 | DOI Listing |
Am J Health Promot
January 2025
College of Social Work, University of South Carolina, Columbia, SC, USA.
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View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
In this research, inspired by the principles of biological visual attention mechanisms and swarm intelligence found in nature, we present an Enhanced Self-Correlation Attention and Multi-Branch Joint Module Network (EMNet), a novel model for few-shot image classification. Few-shot image classification aims to address the problem of image classification when data are limited. Traditional models require a large amount of labeled data for training, while few-shot learning trains models using only a small number of samples (just a few samples per class) to recognize new categories.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
Chula Intelligent and Complex Systems Lab, Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet, identifying the mechanisms that underlie enhanced performance remains challenging, particularly in many-body open systems where nonlinear interactions and dissipation intertwine in complex ways. Here, we investigate a minimal model of a driven-dissipative quantum reservoir described by two coupled Kerr-nonlinear oscillators, an experimentally realizable platform that features controllable coupling, intrinsic nonlinearity, and tunable photon loss.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
Multimedia recommendation systems aim to accurately predict user preferences from multimodal data. However, existing methods may learn a recommendation model from spurious features, i.e.
View Article and Find Full Text PDFJMIR Med Educ
January 2025
Centre for Digital Transformation of Health, University of Melbourne, Carlton, Australia.
Background: Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia.
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