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Record W2002139652 · doi:10.1258/jhsrp.2007.007100

Part of a Global Workforce: Migration of British-Trained Pharmacists

2008· article· en· W2002139652 on OpenAlexaboutno aff
Karen Hassell, Liza Nichols, Peter Noyce

Bibliographic record

VenueJournal of Health Services Research & Policy · 2008
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationWorkforceImmigrationSpouseDestinationsEconomic shortageWork (physics)Health careWork abroadExpatriateMedicineNursingEconomic growthBrain drainPolitical scienceGovernment (linguistics)TourismLaw

Abstract

fetched live from OpenAlex

OBJECTIVES: Many countries, including the UK, have identified a shortage of pharmacists, partly due to emigration. This study was undertaken to examine the extent and nature of migration taking place among British-qualified pharmacists. METHODS: Mixed methods, including secondary analysis of quantitative data, qualitative research and a large self-completion survey of all British-registered pharmacists with an overseas address. RESULTS: Almost 11% of British-registered pharmacists reside overseas. Nearly three-quarters are British-trained and most are UK nationals. The US, Canada and Australia are the main destinations. The majority work as pharmacists in health services, but sizeable proportions are either retired, not working for other reasons or work in industry. Those who emigrate include those returning home, moving for career opportunities, for lifestyle reasons or as a 'spouse trailer'. For many the move abroad is a permanent one. CONCLUSIONS: Great Britain is both a source and destination country for migrating pharmacists. Emigration currently exceeds immigration. Pharmacists are not migrating to developing countries, so the profession may want to consider ways of contributing to the health care systems in developing countries which are the source of some of the immigrant pharmacists to Great Britain.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.178
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.145
GPT teacher head0.559
Teacher spread0.414 · 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 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

Citations8
Published2008
Admission routes1
Has abstractyes

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