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Record W2792758600 · doi:10.1080/10401334.2018.1437040

“They Will Come to Understand”: Supervisor Reflections on International Medical Electives

2018· article· en· W2792758600 on OpenAlexafffund
Erica Roebbelen, Katie Dorman, Andrea Hunter, Christian Kraeker, Tim O’Shea, Nikki Bozinoff

Bibliographic record

VenueTeaching and Learning in Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsSt. Michael's HospitalUniversity of TorontoMcMaster University
FundersMcMaster University
KeywordsIMesContext (archaeology)Qualitative researchMedical educationMedicinePsychologySociology

Abstract

fetched live from OpenAlex

Phenomenon: Increasing numbers of medical students from high-income countries are undertaking international medical electives (IMEs) during their training. Much has been written about the benefits of these experiences for the student, and concerns have been raised regarding the burden of IMEs on host communities. The voices of physicians from low- and middle-income countries who supervise IMEs have not been explored in depth. The current study sought to investigate host–physician perspectives on IMEs. Approach: Host supervisors were recruited by convenience sampling through students travelling abroad for IMEs during the summer of 2012. From 2012 through 2014, 11 semistructured interviews were conducted by telephone with host supervisors from Nepal, Uganda, Ghana, Guyana, and Kenya. Participants were invited to describe their motivations for hosting IMEs and their experiences of the benefits and harms of IMEs. Interviews were transcribed verbatim and checked for accuracy. An initial coding framework was developed and underwent multiple revisions, after which analytic categories were derived using conventional qualitative content analysis. Findings: For host supervisors, visits from international medical students provided a window into the resource-rich medical practice of high-income countries, and supervisors positioned themselves, their education, and clinical expertise against perceived standards of the international students' context. Hosting IMEs also contributed to supervisors' identities as educators connected to a global community. Supervisors described the challenge of helping students navigate their distress when confronting global health inequity. Finally, the desire for increasingly reciprocal relationships was expressed as a hope for the future. Insights: IMEs can be formative for host supervisors' identities and are used to benchmark host institutions compared with international medical standards. Reciprocity was articulated as essential for IMEs moving forward.

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)0.013
metaresearch head score (Gemma)0.038
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 score0.016
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0160.010
Scholarly communication0.0070.004
Open science0.0020.011
Research integrity0.0040.013
Insufficient payload (model declined to judge)0.0040.001

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 head0.049
GPT teacher head0.405
Teacher spread0.356 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

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