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Record W2041091554 · doi:10.1186/1756-0500-5-249

Ego identity development in physicians: a cross-cultural comparison using a mixed method approach

2012· article· en· W2041091554 on OpenAlexaffabout
Tanya Beran, Efrem Violato, Sonia Faremo, Claudio Violato, David Watt, Deidre Lake

Bibliographic record

VenueBMC Research Notes · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLicensureIdentity (music)CategorizationMedicinePsychologyTest (biology)Medical educationFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to examine the career decision-making process of International Medical Graduates (IMGs). There are two main types of IMGs who apply for licensure in Canada. Canadian International Medical Graduates (CIMGs) were Canadian citizens before leaving to study medicine in a foreign country, in comparison to those non-CIMGs who had studied medicine in a foreign country before immigrating to Canada. Given that their motivations for becoming a doctor in Canada may differ, it is important to examine how they decided to become a doctor for each group separately. METHODS: A total of 46 IMGs participated in a semi-structured interview - 20 were CIMGs and 26 were non-CIMGs. RESULTS: An iterative process of content analysis was conducted to categorize responses from five open-ended questions according to the Ego Identity Statuses theory of career decision-making. Event contingency analysis identified a significant difference between CIMGs and non-CIMGs, Fisher's exact test (1) = 18.79, p < .0001. A total of 55% of CIMGs were categorized as identity achieved and 45% as foreclosed; 100% of non-CIMGs were classified as identity foreclosed. CONCLUSION: About half of the Canadian citizens who had studied medicine in a foreign country had explored different careers before making a commitment to medicine, and half had not. No IMGs, however, who studied medicine in another country before immigrating to Canada, had explored various career opportunities before selecting medicine.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.613
GPT teacher head0.674
Teacher spread0.061 · 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 designObservational
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

Citations7
Published2012
Admission routes2
Has abstractyes

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