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Record W2005246312 · doi:10.12927/hcq..17162

Facts and Opinions: Abetting Emigration of Canada's Nurses and Doctors

2005· letter· en· W2005246312 on OpenAlexaffabout
Mark Bernstein

Bibliographic record

VenueHealthcare Quarterly · 2005
Typeletter
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsEmigrationNursingFamily medicineMedicineBest practicePolitical sciencePublic relationsLaw

Abstract

fetched live from OpenAlex

ast year an article appeared in Healthcare Quarterly attempting to analyze and understand reasons nurses emigrate from Canada to the United States, specifically North Carolina (Pink et al. 2004). An introduction to the article appeared unsympathetic to this situation (Pringle 2004). I recently came across the Spring issue of a Canadian-based publication whose mission, as printed in the publication, is “to provide healthcare professionals with job opportunities” among other things. Of the eight large glossy pages constituting the publication, almost two (25%) were devoted to advertisements attracting Canadian nurses to other countries. There were four ads (including one full-page) extolling the virtues of nursing in the United States and one attracting nurses to work in the United Kingdom. I e-mailed the publisher and the editor to gently challenge them on the ethics of doing this but received no response. “Poaching” of healthcare professionals is an old but evergrowing problem, impeding the development of better healthcare in a number of societies, most notably in many developing countries. Singh et al. (2003) articulately analyzed the ethics of this practice in the nursing profession and denounced the practice of developed countries recruiting nurses from developing countries. They also argued that developing nations are not doing enough to address the factors that precipitate nurse migration (Singh et al. 2003). (Although Canada is not a developing country, we can fairly be accused of not doing enough to prevent our own nursing “brain drain.”) Patel (2003) explored the problem, specifically focusing on the recruitment of doctors from India and other developing countries to the UK after they have completed fellowship training in the UK. He suggested some specific requirements for ethical recruitment of doctors from developing countries (Patel 2003). In some situations, leaders in developing countries have formally asked developed countries, including Canada, to recruit fewer of their healthcare professionals (Spurgeon 2001). The Canadian healthcare system is one of the last not-forprofit socialized, one-tiered systems in the world. We are all aware of its shortcomings, such as inappropriately long waits for diagnostic procedures such as magnetic resonance imaging, and for elective surgical procedures such as knee replacement. We are also all aware that, by and large, it is the most humane, fair and efficient system in the world. A recent full-length article in the New York Times Magazine, speculating on American healthcare reform, referred to the Canadian system a number of times, mostly favourably (Lowenstein 2005). Canada and the US are both highly developed countries, so asymmetry in quality of healthcare is not a major issue in drawing healthcare providers from one country to the other. However, there is asymmetry in remuneration, in that nurses attracted to the US from Canada can expect bigger signing bonuses and higher salaries. Additionally, there is often an improvement in personal lifestyle such as weather, improved educational opportunities (as many American hospitals support nurses’ pursuit of advanced education) and other factors (Pink et al. 2004; Pringle 2004). The differential between incomes of Canadian and American doctors, especially specialists, is even more substantial than the differential in nurses’ salaries. Therefore, arguably the main incentive that attracts healthcare providers from Canada to the US is similar to (though scaleddown from) the strong incentive that draws healthcare providers from developing to developed countries. Many of these factors cannot be changed overnight. In spite of that, many Canadians would still prefer to live and work in Canada. Therefore, the brain drain from Canada to the US continues at a steady but limited rate, which does not cripple our universities and hospitals, for example, but it certainly poses ongoing challenges to these and other institutions within our society, which has only one-tenth the human resources to draw on compared to the US. This challenge is arguably greater within the nursing profession than with any other identifiable group of professionals in our country. Canada is a land of opportunity, and no one would ever stand in the way of people seeking a better or more fulfilling life, even if that takes them away from Canada. But my simple question is this: Should Canadians be so tolerant, unworried and even smug about Canada’s future that we actually facilitate this brain drain? Instead of making it ridiculously simple for nurses and doctors to leave, why don’t we make it easier for them to stay? Various half-hearted strategies have been employed to do this over the years, and these initiatives have been incompletely and/or only intermittently successful. Maybe if we try harder we can do better at keeping our valued healthcare professionals within the best healthcare system in the world, and in a country where most of them would likely prefer to remain. This message is directed squarely at government, hospital administrators and others in a position to make the material changes needed to stop the brain drain of nurses and doctors to the US. A financial commitment to secure the future of our healthcare system seems like an investment with positive and crucial benefit to this particular warrior in the healthcare trenches.

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)0.009
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation 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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.080
Threshold uncertainty score0.581

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.040
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0580.016
Scholarly communication0.0150.007
Open science0.0030.007
Research integrity0.0130.022
Insufficient payload (model declined to judge)0.0060.001

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 head0.029
GPT teacher head0.384
Teacher spread0.355 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2005
Admission routes2
Has abstractyes

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