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Record W2762139921

FROM BRAIN DRAIN TO BRAIN TRAIN – A TRANSNATIONAL CASE ANALYSIS OF NIGERIAN MIGRANT HEALTH CARE WORKERS

2017· article· en· W2762139921 on OpenAlexaboutno aff
Sheri Adekola

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsBrain drainHealth careMigrant workersMedicinePolitical sciencePsychologyEconomic growthEconomics
DOInot available

Abstract

fetched live from OpenAlex

This study involves a micro-analysis of the experience of Nigerian-trained health professionals in Canada and is designed to understand the experiences of these skilled migrants, the impact of their migration, and how further migration might be stimulated or reduced through engagement in transnational activities with workers still in Nigeria. The research questions asked, (a) Which discourses of skill exchange are most meaningful to Nigerian health care workers in Canada? (b) How is this process of value exchange and extraction structured by transnational connectivity? (c) How does this research contribute to current concepts regarding skilled migration?\nFramed by the Integrative Model Approach of Koser & Salt (1997), this narrative inquiry used semi-structured interviews, modified surveys with open-ended questions, document analysis, and key informant interviews to collect data from a total of 132 participants. Findings were organized around three concepts – skilled migration discourse, transnationalism, and remittances – used to frame how migrants understand their experience and the consequences of their migration in both the sending and receiving countries.\n“Brain train” – migration for educational purposes – was the skilled migration discourse most often chosen by respondents regardless of gender, occupation, marital status, or prior education. However, older participants (aged 50 and up) tended to identify with “brain circulation” and “brain networking.” Participants reported various levels of engagement in transnational activities such as sending/receiving remittances, gaining new skills, and making charitable donation. Active transnational engagement was shown to increase the likelihood of migration inquiries from non-migrant counterparts. Overall, migrants reported positive outcomes from their migration to Canada, although adjustment periods resulting in loneliness and separation were described. As a consequence of their migration, respondents often pursued higher levels of education in Canada, which improved their career options, increased professional training, and enhanced their independence.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.367
Teacher spread0.335 · 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 designNot applicable
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

Citations2
Published2017
Admission routes1
Has abstractyes

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