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Record W2430186556

Pulse : The greying of Canada‚s medical workforce continues

2000· article· en· W2430186556 on OpenAlexvenueaboutno aff
Lynda Buske

Bibliographic record

VenueCanadian Medical Association Journal · 2000
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWorkforcePopulationDemographyFamily medicineEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

The Canadian Institute for Health Information reports that the average age of Canadian physicians rose over the last 5 years, from 46.3 years in 1995 to 47.2 years in 1999. The proportion of physicians aged 50 to 59 also increased, from 19.6% in 1995 to 22.8% in 1999. The number of physicians younger than 40 fell during the same period, from 33% of the total to 28.1%. Although the overall number of physicians relative to the population has remained stable over the last 5 years at about 185 physicians per 100 000 population, the proportion of family physicians relative to the supply of specialists is decreasing. The number of family physicians per 100 000 population dropped by 3.1% between 1995 and 1999, while the number of specialist physicians per 100 000 population rose by 3.4%. The number of physicians emigrating from Canada continues to decline from the peak levels reached in the mid-1990s, with 585 physicians moving abroad during 1999 and 343 returning. The proportion of specialists migrating (69%) far exceeded that of family physicians (31%), although this has not always been the case. From the mid-1980s to the mid-1990s, the proportion of emigrating family physicians was consistently higher than that of specialists. The average age of physicians leaving the country was 40, while the average age of those returning to active practice in Canada was 41. —

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0090.005
Scholarly communication0.0090.004
Open science0.0020.003
Research integrity0.0130.009
Insufficient payload (model declined to judge)0.0540.009

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 head0.016
GPT teacher head0.344
Teacher spread0.328 · 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.

Study designObservational
DomainIncentives
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

Citations0
Published2000
Admission routes2
Has abstractyes

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