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Record W3198145008 · doi:10.1111/medu.14652

Innovating medical education—Bringing the clinical environment online

2021· article· en· W3198145008 on OpenAlexfundno aff
Eleanora B. Hicks, Jeffrey Kirwan, Christina T. O'Brien, Mary Higgins

Bibliographic record

VenueMedical Education · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersFaculty of Medicine, University of British Columbia
KeywordsMedical educationExperiential learningRelevance (law)PsychologyMedicinePedagogy

Abstract

fetched live from OpenAlex

As a medical student, transitioning from university- to hospital-based learning is a highly anticipated rite of passage. It's a unique experience that challenges students to think critically and practically apply the knowledge they have gained thus far. Unfortunately, the COVID-19 pandemic has changed all this. Not having that first in-person patient interaction can significantly impact a student's ability to consolidate knowledge and engage in experiential and peer assisted learning. As final-year students who had the unique experience of pre-COVID clinical experience and online education, it was our goal to reflect on our experiences and simulate the clinical environment for new students. Using a process called design thinking, our first step was to empathise with the students and understand how their experiences were different to ours. Using this insight and facilitated by academic staff, we synthesised a range of near-peer-assisted clinical learning aids in the form of a clinical guidebook, podcasts and a clinical exam skills teaching video. The guidebook and podcasts covered high-yield clinical scenarios and sample history vignettes with accompanying guidance on how to present the history. We also included tips on approaching patients and taking histories that we had gained during our clinical experience. The neonatal examination video consisted of a head-to-toe examination of a newborn baby supplemented by a voiceover explaining the steps and the relevance of each section. We will be actively looking for student feedback to further refine these learning aids. To design appropriate content, we reflected on our own experiences and what tools were most beneficial to our learning, including learning opportunities that are unique to the clinical environment. We realised the importance of experiential learning in how much you can gain from observation and interacting with patients. These personal interactions are crucial in solidifying concepts learned in the classroom and grounding knowledge in practice. Additionally, we realised that virtual learning means that students miss out on the passive learning obtained from face-to-face interactions with peers, in addition to the social support provided from classmates. Peer-assisted learning is a hidden asset of medical education and through discussion and collaboration further insight and knowledge can be obtained. This was our first experience as educators rather than students. As a physician, dedication to life-long learning is an essential component of one's career, and through our participation in this project, we were able to take that first step into teaching. If anything, COVID-19 has forced medical schools and clinical educators to be creative and innovative in designing and delivering medical curriculum. Although many new avenues of teaching were designed during the pandemic, their application is likely to still be beneficial when clinical placement practice returns to normal. As medical students who have recently engaged in similar clinical modules, we have the unique insight and recent memory of our first hospital experience, and thus, our ability to engage these students in peer assisted learning can provide additional benefits to the standard curriculum.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0140.010
Open science0.0020.020
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0130.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.419
Teacher spread0.394 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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