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Record W4400353226 · doi:10.1186/s12909-024-05702-w

International medical learners and their adjustment after returning to their countries of origin: a qualitative study

2024· article· en· W4400353226 on OpenAlexaffabout
Itthipon Wongprom, Onlak Ruangsomboon, Jikai Huang, Abbas Ghavam-Rassoul

Bibliographic record

VenueBMC Medical Education · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of TorontoSt. Michael's HospitalToronto General HospitalUniversity Health Network
FundersNatureMahidol University
KeywordsMedical educationQualitative researchPsychologyMEDLINEMedicinePolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

INTRODUCTION: International medical trainees, including residents and fellows, must cope with many challenges, such as differences in cultural hierarchical systems, languages, and acceptance. Nonetheless, the need for adjustment perpetuates even after training is completed abroad. When some international trainees return to their countries of origin, they continue to face adjustment challenges due to reverse culture shock. Others must make many further readjustments. This study presents an exploration of the adjustment and coping strategies of international medical learners after returning to their countries of origin upon completion of their programs. METHOD: This study employed a qualitative approach grounded in interpretivism and utilised inductive thematic analysis following Braun and Clarke's method. Semi-structured, in-depth individual interviews were employed to explore the participants' coping strategies. Participants included international medical learners who were (1) international medical graduates who had already returned to their countries of origin, (2) non-Canadian citizens or nonpermanent residents by the start of the programs, and (3) previously enrolled in a residency or fellowship training programme at the University of Toronto, Ontario, Canada. RESULTS: Seventeen participants were included. Three main themes and seven subthemes were created from the analysis and are represented by the Ice Skater Landing Model. According to this model, there are three main forces in coping processes upon returning home: driving, stabilising, and situational forces. The sum and interaction of these forces impact the readjustment process. CONCLUSION: International medical learners who have trained abroad and returned to their countries of origin often struggle with readjustment. An equilibrium between the driving and stabilising forces is crucial for a smooth transition. The findings of this study can help stakeholders better understand coping processes. As healthy coping processes are related to job satisfaction and retention, efforts to support and shorten repatriation adjustment are worthwhile.

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)0.010
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 score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.006
Scholarly communication0.0050.003
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.516
Teacher spread0.462 · 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".

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Citations3
Published2024
Admission routes2
Has abstractyes

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