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Record W4318381700 · doi:10.5116/ijme.63c3.e6b3

International Medical Graduates' perceptions about residency training experience: a qualitative study

2023· article· en· W4318381700 on OpenAlexaff
Marghalara Rashid, Julie My Van Nguyen, Jessica L. Foulds, Gordana Djordjević, Sarah Forgie

Bibliographic record

VenueInternational Journal of Medical Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIMGThematic analysisMedical educationQualitative researchPsychologyStigma (botany)PerceptionMedicineSociologyPsychiatry

Abstract

fetched live from OpenAlex

Objectives: To explore International Medical Graduates residents' experiences and perspectives of their residency training program. Methods: This qualitative study was conducted at a large research-intensive University. Purposeful sampling was used to recruit 14 International medical graduates. The residents recruited for this study were at different levels in their training ranging from Postgraduate year one to five. Residents interviewed represented seven unique specialties. Each trainee was interviewed, and the data were recorded and transcribed verbatim. A thematic analysis framework was used to conduct the data analysis, resulting in the development of study themes. Results: Our analysis generated six main themes. These themes were related to costly decisions, unspoken expectations, the stigma associated with being an IMG, fears of being an IMG, the strength and resilience of IMGs, and recommen-dations proposed by IMGs for program improvement. Conclusions: In this study, we wanted to explore international residents' experiences with their programs. The experience of each individual international resident is unique. However, in this study, we were able to provide firsthand perceptions of IMGs from a research-intensive university and identified common themes experienced and perceived by our resi-dents. This study's findings may help educate, reduce stigma, and guide the implementation of effective individu-al and systemic support for these trainees. Which in turn will enhance the overall educational experiences for IMGs trainees. Our study found that themes seem to be recur-ring, hence, an urgency to bring about appropriate chang-es, equitable opportunities, and support for IMGs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0180.001

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 head0.146
GPT teacher head0.625
Teacher spread0.479 · 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 teacher head, not a consensus.

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

Citations11
Published2023
Admission routes1
Has abstractyes

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