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Record W2023101342 · doi:10.2310/7070.2004.03014

Otolaryngology Manpower in Canada: A Crisis in the Making?

2004· article· en· W2023101342 on OpenAlexaffvenueabout
Everton Gooden, Dale Brown

Bibliographic record

VenueThe Journal of Otolaryngology · 2004
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of TorontoUniversity Health NetworkNorth York General Hospital
Fundersnot available
KeywordsMedicineOtorhinolaryngologyGovernment (linguistics)PopulationFamily medicineEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Physician manpower issues have been of interest to Canadians and government officials for several decades. Since the first otolaryngology manpower survey was completed by Dr. Percy Ireland in 1962, there have been progressive declines in the physician-to-population ratio across Canada from 1 in 42 000 in 1962 to approximately 1 in 75 000 in 2000. The expected increase in our population over the next decade, the cutbacks in medical school enrollment, and an aging population will compound this problem. The system is in crisis, and this country is desperately in need of more otolaryngologists.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.996
Threshold uncertainty score0.699

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0200.008
Scholarly communication0.0090.005
Open science0.0040.004
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0170.002

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 head0.371
Teacher spread0.342 · 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

Citations4
Published2004
Admission routes3
Has abstractyes

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