MétaCan
Menu
Back to cohort
Record W2624648187 · doi:10.1177/0840470417698486

Assessing the prospects for physician supply and demand in Canada: Wishing it was rocket science

2017· article· en· W2624648187 on OpenAlexaffabout
Owen Adams, Tara S. Chauhan, Lynda Buske

Bibliographic record

VenueHealthcare Management Forum · 2017
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsCanadiana.orgCanadian Medical Association
Fundersnot available
KeywordsRocket (weapon)Supply and demandPhysician supplyEngineering ethicsBusinessMedical educationPolitical scienceMedicineEngineeringAeronauticsEconomicsHealth careLaw

Abstract

fetched live from OpenAlex

"It's not rocket science" is an often used phrase to describe tasks that are not very difficult. Although rocketry has proven to be an exacting science with highly predictable results, the same cannot be said for physician workforce planning in Canada. The "boom" in physician supply in the 1960s and 1970s was followed by a "bust" in the early 1990s and a further boom in the 2000s. A large generational shift in the physician population is anticipated between now and 2030; the proportion of "boomers" (1946-1964) will drop from 43% to 16% of the practising profession. Canada has not been alone in increasing physician supply. Any judgement as to whether too many or too few physicians are being trained must consider the drivers and mitigators of both supply and demand. Although there are current concerns about a shortage of practice opportunities for some specialties, the available data do not indicate a physician surplus on the horizon in Canada.

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)0.002
metaresearch head score (Gemma)0.011
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 score0.912
Threshold uncertainty score0.637

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.281
Teacher spread0.265 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueHealthcare Management ForumSame topicBiomedical and Engineering EducationFrench-language works237,207