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Record W2999998484 · doi:10.15694/mep.2020.000016.1

Lessons from across the pond: Student perspectives on the Internal Medicine clerkship experience at an Irish and Canadian medical school

2020· article· en· W2999998484 on OpenAlexaffabout
Ryan T. Sless, Nathaniel E. Hayward, Paul M. Ryan, Adam Kovacs‐Litman, Umberin Najeeb

Bibliographic record

VenueMedEdPublish · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIrishMedical educationExperiential learningMedicineQuality (philosophy)Medical schoolPsychologyPedagogy

Abstract

fetched live from OpenAlex

This article was migrated. The article was marked as recommended. There is an increasing number of Canadians studying medicine outside of Canada, with a large cohort studying in Ireland. Studying abroad often means different foci in medical training which may make transitioning to residency in a different system more challenging. Students often enter North American elective rotations with little knowledge of student roles and responsibilities. This paper provides insight into the differences in learning objectives and student experiences in an Internal Medicine clerkship at a medical school in Canada and Ireland. Learning objectives are similar between systems; but there is an experiential discordance. In Ireland, clerks see many different patients, gaining exposure to a breadth of topics and clinical signs, but medical student presentations rarely inform decisions around patient care. In Canada, clerks have more direct patient responsibilities, performing physical examinations, reviewing investigations, writing progress notes, and devising management plans as part of their professional development. Overall, the Irish system places emphasis on the mastery of core clinical skills and maximizing breadth of patient exposure whereas the Canadian clerkship is more focused on graduated responsibility and formulating management plans, at the expense of some breadth of exposure. Such discrepancies may not affect the quality of residents, but are important considerations for Canadians studying abroad when repatriating for electives and residencies.

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)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.687

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0320.010
Scholarly communication0.0140.003
Open science0.0030.012
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0060.001

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 head0.108
GPT teacher head0.492
Teacher spread0.384 · 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 designQualitative
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

Citations2
Published2020
Admission routes2
Has abstractyes

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