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Record W2111975009 · doi:10.1093/jrs/feq047

'Garang's Seeds': Influences on the Return of Sudanese-Canadian Refugee Physicians to Post-Conflict South Sudan

2011· article· en· W2111975009 on OpenAlexaffabout
Jim L. Finlay, Rodney Crutcher, Neil Drummond

Bibliographic record

VenueJournal of Refugee Studies · 2011
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsHealth Sciences CentreUniversity of Calgary
Fundersnot available
KeywordsRefugeePolitical scienceGender studiesSocioeconomicsGeographySociologyLaw

Abstract

fetched live from OpenAlex

There is a paucity of qualitative research in the intersection of refugee return studies and the health professional ‘brain drain’ literature. This study uses a multiple life history approach to examine factors influencing the return of 15 Sudanese physicians to their home country. In 2006 the University of Calgary and Samaritan’s Purse Canada implemented a training programme for these 15 refugees from Sudan exiled since 1985 who had trained as physicians in Cuba. We found that barriers to their return were overcome by programme assistance with financial obligations, navigation of Canadian government processes involving citizenship, and English language and medical skills upgrading. The arrival of these individuals in their homeland where they currently practise medicine required the sustained alignment of returnee commitment and motivation to return with significant support. These findings highlight the potential for targeted return assistance programmes in stimulating a reversal of ‘brain drain’ migration.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.687
Threshold uncertainty score0.630

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.007
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.108
GPT teacher head0.413
Teacher spread0.305 · 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

Citations10
Published2011
Admission routes2
Has abstractyes

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