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Record W2134667200 · doi:10.12927/hcpap.2010.21795

Internationally Educated Health Professionals: Workforce Integration and Retention

2010· article· en· W2134667200 on OpenAlexaffvenueabout
Andrea Baumann, Jennifer Blythe, Dana Ross

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2010
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsHealth Sciences Centre
Fundersnot available
KeywordsWorkforceEconomic shortageHealth professionalsBusinessHealth carePublic relationsFace (sociological concept)Health professionsCareer PathwaysWorkforce planningNursingMedical educationMedicinePolitical scienceEconomic growthSociologyEconomicsGovernment (linguistics)

Abstract

fetched live from OpenAlex

It is essential that internationally educated healthcare professionals (IEHPs) residing in Canada re-enter and remain in their profession. To make the most of this important supply of healthcare professionals, it is vital to understand who IEHPs are, the challenges they face and how to facilitate their entry and integration into the workforce. In this article, after a summary of what is known of IEHPs who migrate to Canada, common problems of entry and integration into the workforce are discussed. Profession-specific challenges are considered, including how roles in certain professions vary globally and the importance of cultural and communication competencies. Resources to assist physicians and nurses are described and compared with those available for other professions. Finally, future possibilities and strategies for workforce integration are considered. Although the focus in this paper is on one province, the issues and strategies discussed are relevant to other provincial and international jurisdictions that are struggling with shortages and trying to capitalize on potential sources of workforce supply.

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)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.374

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.002
Scholarly communication0.0050.003
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.077
GPT teacher head0.446
Teacher spread0.368 · 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 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

Citations20
Published2010
Admission routes3
Has abstractyes

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Same venueA Nudge Too Far? A Nudge at All? On Paying People to Be HealthySame topicGlobal Health Workforce IssuesFrench-language works237,207