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Record W2033570566 · doi:10.4236/ojpm.2015.53015

International Health Professional Migration and Brain Waste: A Situation of Double-Jeopardy

2015· article· en· W2033570566 on OpenAlexafffund
Nazmul Alam, Lisa Merry, Mohammad Mainul Islam, Claudia Z. Cortijo

Bibliographic record

VenueOpen Journal of Preventive Medicine · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMcGill UniversityUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsWorkforceDouble jeopardyBrain drainBusinessWork (physics)Health careImmigrationHealth professionalsAction (physics)Compensation (psychology)Public relationsNursingEconomic growthMedicinePsychologyPolitical scienceEconomicsSocial psychology

Abstract

fetched live from OpenAlex

The migration of health professionals from low- and middle-income to high-income countries has received much attention amongst the global health community as an important factor influencing health care systems. There is however, much less dialogue about internationally trained health professionals who are not able to practice their professions in their countries of destination, a phenomenon labelled as “brain waste”. It has been shown that the integration of internationally trained health professionals in their country of destination is hindered due to inadequate language skills, a lack of local work experience, cultural incompetency, and barriers to the recognition of credentials from foreign academics and professionals. To maximize gains from migration of health professionals and to minimize the negative impacts, we need policies with proper guidelines for practical strategies to better integrate health professional immigrants into the workforce of destination countries. These policies and action plans should also foster healthcare system capacity building and appropriate compensation in low- and middle-income countries

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.122
GPT teacher head0.519
Teacher spread0.397 · 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 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

Citations10
Published2015
Admission routes2
Has abstractyes

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