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Record W2549599341

Navigating otherness and belonging : A comparative case study of IMGs’ professional integration in Canada and Sweden

2015· article· en· W2549599341 on OpenAlexaboutno aff
Elena Neiterman, Lisa Salmonsson, Ivy Lynn Bourgeault

Bibliographic record

VenueDiVA (Mälardalen University College) · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsGeographySociology
DOInot available

Abstract

fetched live from OpenAlex

This paper explores the othering processes and feelings of belonging among international medical graduates (IMGs) who seek to practise medicine in Canada and Sweden. Building on the theoretical literature on othering, belonging, and the conceptualisation of status dilemmas, we explore how IMGs in Canada and Sweden negotiate their professional identity, how they cope with being othered and how they establish a path to belonging. Analysing qualitative interviews with 15 Swedish and 67 Canadian immigrant physicians, who are either practising medicine or are in the process of professional integration, we demonstrate that the construction of professional identity among IMGs necessitates constant comparison between the differences and similarities among ‘us’ – immigrant physicians, and ‘them’ – local doctors. In this process, one’s ethnicity, gender, and professional status are intertwined with the experience of being seen as ‘the Other’. We also show that in negotiating their professional status, IMGs actively interpret the meaning of being a Canadian/Swedish physician. We conclude that feelings of belonging to a professional group (Canadian or Swedish) do not seem to be static but rather fluid, ephemeral and changing, depending on the context. Our analysis suggests that more attention should be paid to the social context in which experiences of processes of being othered and feeling belonging are being constructed and interpreted by people themselves.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.392
Teacher spread0.325 · 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.

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

Citations9
Published2015
Admission routes1
Has abstractyes

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